From 4230ef2751fd9a3fdc07963410706b26b7512a32 Mon Sep 17 00:00:00 2001 From: Abdelrahman Ahmed Date: Wed, 17 Apr 2019 14:27:05 +0200 Subject: [PATCH 01/53] updated add_neighbor and add_edge method so that the graph is weighted --- graph_adjacency-list.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/graph_adjacency-list.py b/graph_adjacency-list.py index fec2f958..ebc3f47c 100644 --- a/graph_adjacency-list.py +++ b/graph_adjacency-list.py @@ -4,9 +4,9 @@ def __init__(self, n): self.name = n self.neighbors = list() - def add_neighbor(self, v): + def add_neighbor(self, v, weight): if v not in self.neighbors: - self.neighbors.append(v) + self.neighbors.append((v, weight)) self.neighbors.sort() class Graph: @@ -19,11 +19,11 @@ def add_vertex(self, vertex): else: return False - def add_edge(self, u, v): + def add_edge(self, u, v, weight=0): if u in self.vertices and v in self.vertices: # my YouTube video shows a silly for loop here, but this is a much faster way to do it - self.vertices[u].add_neighbor(v) - self.vertices[v].add_neighbor(u) + self.vertices[u].add_neighbor(v, weight) + self.vertices[v].add_neighbor(u, weight) return True else: return False From 8098344f23ff7b3790f566db00913c77ea087ff1 Mon Sep 17 00:00:00 2001 From: tomas2310328 Date: Fri, 26 Apr 2019 20:07:29 +0200 Subject: [PATCH 02/53] Queues implementaion --- Queues implementaion.py | 73 +++++++++++++++++++++++++++++++++++++++++ 1 file changed, 73 insertions(+) create mode 100644 Queues implementaion.py diff --git a/Queues implementaion.py b/Queues implementaion.py new file mode 100644 index 00000000..da9bad1a --- /dev/null +++ b/Queues implementaion.py @@ -0,0 +1,73 @@ + +# implemented by Linked list +class Node(object): + def __init__(self, item = None): + self.item = item + self.next = None + self.previous = None + + +class Queue(object): + def __init__(self): + self.length = 0 + self.head = None + self.tail = None + + def enqueue(self, x): + newNode = Node(x) + if self.head == None: + self.head = self.tail = newNode + else: + self.tail.next = newNode + newNode.previous = self.tail + self.tail = newNode + self.length += 1 + + + def dequeue (self): + item = self.head.item + self.head = self.head.next + self.length -= 1 + if self.length == 0: + self.last = None + return item + + +################################################# + +# implemented by array +class Queue: + def __init__(self): + self.items = [] + + def is_empty(self): + return self.items == [] + + def enqueue(self, data): + self.items.append(data) + + def dequeue(self): + return self.items.pop(0) + + def display(self): + ar = [] + for i in self.items: + ar.append(i) + return ar + + + + + + + + + +que = Queue() +que.enqueue('google') +que.enqueue('youtube') +que.enqueue('udemy') +que.enqueue('udacity') +que.dequeue() +que.dequeue() +print(que.display()) From 7bf677341359514d9611fa41599bef3b10a264cf Mon Sep 17 00:00:00 2001 From: Manthan Gupta <42516515+Manthan109@users.noreply.github.com> Date: Tue, 1 Oct 2019 20:56:50 +0530 Subject: [PATCH 03/53] Added comments --- BinaryToDecimal.py | 22 +++++++++++----------- 1 file changed, 11 insertions(+), 11 deletions(-) diff --git a/BinaryToDecimal.py b/BinaryToDecimal.py index ac41308d..1c3096c8 100644 --- a/BinaryToDecimal.py +++ b/BinaryToDecimal.py @@ -1,25 +1,25 @@ # Python: Binary to Decimal Conversion # binToDec and decToBin functions are rendered obsolete by the universal convert function -def binToDec(binNum): +def binToDec(binNum): #function created to convert binary to decimal with parametere binNum decNum = 0 power = 0 - while binNum > 0: - decNum += 2 ** power * (binNum % 10) - binNum //= 10 - power += 1 + while binNum > 0: #loop will run till binNum is greater than 0 + decNum += 2 ** power * (binNum % 10) + binNum //= 10 # reducing binNum everytime by 1 digit + power += 1 # increasing power by 1 each loop return decNum -def decToBin(decNum): +def decToBin(decNum): #function created to convert decimal to binary with parametere decNum binNum = 0 power = 0 - while decNum > 0: + while decNum > 0:#loop will run till decNum is greater than 0 binNum += 10 ** power * (decNum % 2) - decNum //= 2 - power += 1 + decNum //= 2 # reducing decNum everytime by 1 digit + power += 1 # increasing power by 1 each loop return binNum -def convert(fromNum, fromBase, toBase): +def convert(fromNum, fromBase, toBase): #function for converting from any base to any other base toNum = 0 power = 0 while fromNum > 0: @@ -31,4 +31,4 @@ def convert(fromNum, fromBase, toBase): # print (str(binToDec(101011))) # print (str(decToBin(128))) print (str(convert(127, 10, 8))) # converts 127 in base 10 to base 8 -print (str(convert(101001, 2, 2))) \ No newline at end of file +print (str(convert(101001, 2, 2))) From 38f05bd2f07002f05cdec65b966426dcf21e48ec Mon Sep 17 00:00:00 2001 From: Abhishek garg <33170757+abhi01274@users.noreply.github.com> Date: Sun, 20 Oct 2019 15:41:00 +0530 Subject: [PATCH 04/53] Update Primes.py --- Primes.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/Primes.py b/Primes.py index 196644d3..3bbf14eb 100644 --- a/Primes.py +++ b/Primes.py @@ -2,7 +2,7 @@ max = int(input("Find primes up to what number? : ")) primeList = [] - +#for loop for checking each number for x in range(2, max + 1): isPrime = True index = 0 @@ -43,4 +43,4 @@ x += 1 -print(primeList) \ No newline at end of file +print(primeList) From a073c629bc57706872daffbf7c321c310f810d5f Mon Sep 17 00:00:00 2001 From: ChelseyOSU <50436670+ChelseyOSU@users.noreply.github.com> Date: Fri, 22 May 2020 01:31:09 -0700 Subject: [PATCH 05/53] Fix bug The current code would fail when remove the root element in a tree where right subtree only have right child. # 17 # 0 20 # -5 5 25 Current code would produce a result with duplicate elements # 20 # 0 20 # -5 5 25 Trick is to move one the assignment to root value to the bottom --- Trees/bst.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/Trees/bst.py b/Trees/bst.py index 9137b4b8..f45b2f4f 100644 --- a/Trees/bst.py +++ b/Trees/bst.py @@ -131,7 +131,6 @@ def remove(self, data): delNodeParent = delNode delNode = delNode.leftChild - self.root.value = delNode.value if delNode.rightChild: if delNodeParent.value > delNode.value: delNodeParent.leftChild = delNode.rightChild @@ -142,6 +141,7 @@ def remove(self, data): delNodeParent.leftChild = None else: delNodeParent.rightChild = None + self.root.value = delNode.value return True @@ -233,4 +233,4 @@ def main(): print(bst.remove(10)) bst.preorder() -main() \ No newline at end of file +main() From b1828a7d0117ac2c910df827047f196482095eeb Mon Sep 17 00:00:00 2001 From: Joe James Date: Mon, 25 May 2020 17:45:08 -0700 Subject: [PATCH 06/53] Initial upload --- Python List Iteration.ipynb | 197 +++++++++++++++++++ Python Set Comprehensions.ipynb | 331 ++++++++++++++++++++++++++++++++ 2 files changed, 528 insertions(+) create mode 100644 Python List Iteration.ipynb create mode 100644 Python Set Comprehensions.ipynb diff --git a/Python List Iteration.ipynb b/Python List Iteration.ipynb new file mode 100644 index 00000000..d680f54f --- /dev/null +++ b/Python List Iteration.ipynb @@ -0,0 +1,197 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Python List Iteration\n", + "A variety of ways to iterate Lists, including for loop, while loop, enumerate." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "----\n", + "The standard for loop works great if inside the loop you only need the item and not its index." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "a\n", + "b\n", + "c\n", + "d\n", + "e\n" + ] + } + ], + "source": [ + "letters = ['a', 'b', 'c', 'd', 'e']\n", + "\n", + "for letter in letters:\n", + " print(letter)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "----\n", + "If you need the index inside the loop you can use range(len(list)). \n", + "Then you can always get the list item if needed by using the index." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "letters 0 = a\n", + "letters 1 = b\n", + "letters 2 = c\n", + "letters 3 = d\n", + "letters 4 = e\n" + ] + } + ], + "source": [ + "for index in range(len(letters)):\n", + " print('letters', index, '=', letters[index])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "----\n", + "Best option if you need both index and item inside the loop is to use Python's **enumerate** function. \n", + "Enumerate works in both Python 2.x and 3.x" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "letters 0 = a\n", + "letters 1 = b\n", + "letters 2 = c\n", + "letters 3 = d\n", + "letters 4 = e\n" + ] + } + ], + "source": [ + "for index, item in enumerate(letters):\n", + " print('letters', index, '=', item)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Enumerate actually returns an iterable enumerate object, \n", + "which is a sequence of tuples of (index, item)." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(0, 'a')\n", + "(1, 'b')\n", + "\n" + ] + } + ], + "source": [ + "enum_obj = enumerate(letters)\n", + "print(next(enum_obj))\n", + "print(next(enum_obj))\n", + "print(type(enum_obj))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "----\n", + "Probably the clumsiest way to iterate a list in Python -- the **while loop**. \n", + "Requires index initialization before list, and incrementation inside loop." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "letters 0 = a\n", + "letters 1 = b\n", + "letters 2 = c\n", + "letters 3 = d\n", + "letters 4 = e\n" + ] + } + ], + "source": [ + "index = 0\n", + "while index < len(letters): \n", + " print('letters', index, '=', letters[index]) \n", + " index += 1" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "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": "3.7.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Python Set Comprehensions.ipynb b/Python Set Comprehensions.ipynb new file mode 100644 index 00000000..475521cb --- /dev/null +++ b/Python Set Comprehensions.ipynb @@ -0,0 +1,331 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Python Set Comprehensions\n", + "Note that Python sets are not ordered, and duplicates are automatically removed. \n", + "Otherwise, comprehensions work just like with lists. \n", + "General syntax is: new_set = {expression for item in iterable if condition}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Simple Comprehension using Range" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{0, 1, 2, 3, 4, 5, 6, 7, 8, 9}\n" + ] + } + ], + "source": [ + "ints = {i for i in range(10)}\n", + "print(ints)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Comprehension using Range with a Condition filter\n", + "Only take even values from range" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{0, 2, 4, 6, 8}\n" + ] + } + ], + "source": [ + "evens = {i for i in range(10) if i%2 == 0}\n", + "print(evens)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Apply math function to values in range\n", + "Here, square each value" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{0, 1, 64, 4, 36, 9, 16, 49, 81, 25}\n" + ] + } + ], + "source": [ + "squares = {i*i for i in range(10)}\n", + "print(squares)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that Python eliminates duplicates from sets" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{0, 1, 4, 9, 16, 25}\n" + ] + } + ], + "source": [ + "sqrs = {i*i for i in range(-5, 5)}\n", + "print(sqrs)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Set Comprehension on a List" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{4, 9, 169, 49, 121, 25}\n" + ] + } + ], + "source": [ + "primes = [2, 2, 2, 3, 3, 5, 5, 5, 7, 11, 11, 13, 13, 13, 13]\n", + "primes_squared = {p*p for p in primes}\n", + "print(primes_squared)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### More Complex Expressions: quadratic transformation\n", + "Any expression is allowed. More complex expressions can be put in parentheses. \n", + "Here, quadratic equation: \n", + "2x^2 + 5x + 10" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{43, 143, 307, 85, 28, 413}\n" + ] + } + ], + "source": [ + "transformed = {(2*x*x + 5*x + 10) for x in primes}\n", + "print(transformed)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Flatten List and eliminate duplicates\n", + "Syntax: {leaf for branch in tree for leaf in branch}" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{1, 2, 3, 98, 76}\n" + ] + } + ], + "source": [ + "nums = [[1,3],[2,3],[3,98],[76,1]]\n", + "flat_set = {a for b in nums for a in b}\n", + "print(flat_set)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Eliminate Dups from a List\n", + "We can easily eliminate differences in capitalization, while removing duplicates." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'Albert', 'Ella', 'George', 'Salil'}\n" + ] + } + ], + "source": [ + "names = ['salil', 'ALBERT', 'Ella', 'george', 'Salil', 'George', 'ELLA', 'Albert']\n", + "names_set = {n.capitalize() for n in names}\n", + "print(names_set)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And it's easy to convert this back to a list." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['Albert', 'Ella', 'George', 'Salil']\n" + ] + } + ], + "source": [ + "names_set = list({n.capitalize() for n in names})\n", + "print(names_set)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Get Car Make from list of Make & Model\n", + "We're getting the first word from each string." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'Toyota', 'Tesla', 'Chevy'}\n" + ] + } + ], + "source": [ + "cars = ['Toyota Prius', 'Chevy Bolt', 'Tesla Model 3', 'Tesla Model Y']\n", + "makes = {(c.split()[0]) for c in cars}\n", + "print(makes)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Get Initials from Names\n", + "Take first and last initials" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'CB', 'NF', 'HP'}\n" + ] + } + ], + "source": [ + "names = ['Clint Barton', 'Tony', 'Nick Fury', 'Hank Pym']\n", + "inits = {(n.split()[0][0] + n.split()[1][0]) for n in names if len(n.split())==2}\n", + "print(inits)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "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": "3.7.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 0d97aa6a0be7938571078ddc0c35938fecbdce97 Mon Sep 17 00:00:00 2001 From: Joe James Date: Mon, 25 May 2020 22:15:53 -0700 Subject: [PATCH 07/53] Stack queue heap file upload --- Stacks, Queues & Heaps.ipynb | 346 +++++++++++++++++++++++++++++++++++ 1 file changed, 346 insertions(+) create mode 100644 Stacks, Queues & Heaps.ipynb diff --git a/Stacks, Queues & Heaps.ipynb b/Stacks, Queues & Heaps.ipynb new file mode 100644 index 00000000..a54ede21 --- /dev/null +++ b/Stacks, Queues & Heaps.ipynb @@ -0,0 +1,346 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Stacks, Queues & Heaps\n", + "© Joe James, 2019.\n", + "\n", + "### Stack using Python List\n", + "Stack is a LIFO data structure -- last-in, first-out. \n", + "Use append() to push an item onto the stack. \n", + "Use pop() to remove an item." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[4, 7, 12, 19]\n" + ] + } + ], + "source": [ + "my_stack = list()\n", + "my_stack.append(4)\n", + "my_stack.append(7)\n", + "my_stack.append(12)\n", + "my_stack.append(19)\n", + "print(my_stack)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "19\n", + "12\n", + "[4, 7]\n" + ] + } + ], + "source": [ + "print(my_stack.pop())\n", + "print(my_stack.pop())\n", + "print(my_stack)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Stack using List with a Wrapper Class\n", + "We create a Stack class and a full set of Stack methods. \n", + "But the underlying data structure is really a Python List. \n", + "For pop and peek methods we first check whether the stack is empty, to avoid exceptions." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "class Stack():\n", + " def __init__(self):\n", + " self.stack = list()\n", + " def push(self, item):\n", + " self.stack.append(item)\n", + " def pop(self):\n", + " if len(self.stack) > 0:\n", + " return self.stack.pop()\n", + " else:\n", + " return None\n", + " def peek(self):\n", + " if len(self.stack) > 0:\n", + " return self.stack[len(self.stack)-1]\n", + " else:\n", + " return None\n", + " def __str__(self):\n", + " return str(self.stack)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Test Code for Stack Wrapper Class" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1, 3]\n", + "3\n", + "1\n", + "1\n", + "None\n" + ] + } + ], + "source": [ + "my_stack = Stack()\n", + "my_stack.push(1)\n", + "my_stack.push(3)\n", + "print(my_stack)\n", + "print(my_stack.pop())\n", + "print(my_stack.peek())\n", + "print(my_stack.pop())\n", + "print(my_stack.pop())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "### Queue using Python Deque\n", + "Queue is a FIFO data structure -- first-in, first-out. \n", + "Deque is a double-ended queue, but we can use it for our queue. \n", + "We use append() to enqueue an item, and popleft() to dequeue an item. \n", + "See [Python docs](https://docs.python.org/3/library/collections.html#collections.deque) for deque." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "deque([5, 10])\n", + "5\n" + ] + } + ], + "source": [ + "from collections import deque\n", + "my_queue = deque()\n", + "my_queue.append(5)\n", + "my_queue.append(10)\n", + "print(my_queue)\n", + "print(my_queue.popleft())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Fun exercise:\n", + "Write a wrapper class for the Queue class, similar to what we did for Stack, but using Python deque. \n", + "Try adding enqueue, dequeue, and get_size methods." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Python Single-ended Queue Wrapper Class using Deque\n", + "We rename the append method to enqueue, and popleft to dequeue. \n", + "We also add peek and get_size operations." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "from collections import deque\n", + "class Queue():\n", + " def __init__(self):\n", + " self.queue = deque()\n", + " self.size = 0\n", + " def enqueue(self, item):\n", + " self.queue.append(item)\n", + " self.size += 1\n", + " def dequeue(self, item):\n", + " if self.size > 0:\n", + " self.size -= 1\n", + " return self.queue.popleft()\n", + " else: \n", + " return None\n", + " def peek(self):\n", + " if self.size > 0:\n", + " ret_val = self.queue.popleft()\n", + " queue.appendleft(ret_val)\n", + " return ret_val\n", + " else:\n", + " return None\n", + " def get_size(self):\n", + " return self.size" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Python MaxHeap\n", + "A MaxHeap always bubbles the highest value to the top, so it can be removed instantly. \n", + "Public functions: push, peek, pop \n", + "Private functions: __swap, __floatUp, __bubbleDown, __str__." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "class MaxHeap:\n", + " def __init__(self, items=[]):\n", + " super().__init__()\n", + " self.heap = [0]\n", + " for item in items:\n", + " self.heap.append(item)\n", + " self.__floatUp(len(self.heap) - 1)\n", + "\n", + " def push(self, data):\n", + " self.heap.append(data)\n", + " self.__floatUp(len(self.heap) - 1)\n", + "\n", + " def peek(self):\n", + " if self.heap[1]:\n", + " return self.heap[1]\n", + " else:\n", + " return False\n", + " \n", + " def pop(self):\n", + " if len(self.heap) > 2:\n", + " self.__swap(1, len(self.heap) - 1)\n", + " max = self.heap.pop()\n", + " self.__bubbleDown(1)\n", + " elif len(self.heap) == 2:\n", + " max = self.heap.pop()\n", + " else: \n", + " max = False\n", + " return max\n", + "\n", + " def __swap(self, i, j):\n", + " self.heap[i], self.heap[j] = self.heap[j], self.heap[i]\n", + "\n", + " def __floatUp(self, index):\n", + " parent = index//2\n", + " if index <= 1:\n", + " return\n", + " elif self.heap[index] > self.heap[parent]:\n", + " self.__swap(index, parent)\n", + " self.__floatUp(parent)\n", + "\n", + " def __bubbleDown(self, index):\n", + " left = index * 2\n", + " right = index * 2 + 1\n", + " largest = index\n", + " if len(self.heap) > left and self.heap[largest] < self.heap[left]:\n", + " largest = left\n", + " if len(self.heap) > right and self.heap[largest] < self.heap[right]:\n", + " largest = right\n", + " if largest != index:\n", + " self.__swap(index, largest)\n", + " self.__bubbleDown(largest)\n", + " \n", + " def __str__(self):\n", + " return str(self.heap)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### MaxHeap Test Code" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0, 95, 10, 21, 3]\n", + "95\n", + "21\n" + ] + } + ], + "source": [ + "m = MaxHeap([95, 3, 21])\n", + "m.push(10)\n", + "print(m)\n", + "print(m.pop())\n", + "print(m.peek())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "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": "3.7.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 1d0b24eff0aa445b906750d070d6233fa32fc721 Mon Sep 17 00:00:00 2001 From: Joe James Date: Wed, 27 May 2020 10:01:14 -0700 Subject: [PATCH 08/53] added import json statement --- Web Data Mining/Python Requests.ipynb | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/Web Data Mining/Python Requests.ipynb b/Web Data Mining/Python Requests.ipynb index 6b9274ec..66f27fed 100644 --- a/Web Data Mining/Python Requests.ipynb +++ b/Web Data Mining/Python Requests.ipynb @@ -19,7 +19,8 @@ "metadata": {}, "outputs": [], "source": [ - "import requests" + "import requests\n", + "import json" ] }, { From b038e432cd2bd352a29dfa8b7df7d6481ccb0bee Mon Sep 17 00:00:00 2001 From: Joe James Date: Wed, 27 May 2020 12:31:59 -0700 Subject: [PATCH 09/53] Revised QuickSort code, in Jupyter nb --- Sorting Algorithms/Python QuickSort.ipynb | 140 ++++++++++++++++++++++ 1 file changed, 140 insertions(+) create mode 100644 Sorting Algorithms/Python QuickSort.ipynb diff --git a/Sorting Algorithms/Python QuickSort.ipynb b/Sorting Algorithms/Python QuickSort.ipynb new file mode 100644 index 00000000..5f925c81 --- /dev/null +++ b/Sorting Algorithms/Python QuickSort.ipynb @@ -0,0 +1,140 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Python QuickSort Algorithm" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[5, 9, 1, 2, 4, 8, 6, 3, 7]\n", + "[1, 2, 3, 4, 5, 6, 7, 8, 9]\n" + ] + } + ], + "source": [ + "#---------------------------------------\n", + "# Quick Sort\n", + "#---------------------------------------\n", + "def quick_sort(A):\n", + " quick_sort2(A, 0, len(A)-1)\n", + " \n", + "def quick_sort2(A, low, hi):\n", + " if hi-low < 1 and low < hi:\n", + " quick_selection(A, low, hi)\n", + " elif low < hi:\n", + " p = partition(A, low, hi)\n", + " quick_sort2(A, low, p - 1)\n", + " quick_sort2(A, p + 1, hi)\n", + " \n", + "def get_pivot(A, low, hi):\n", + " mid = (hi + low) // 2\n", + " s = sorted([A[low], A[mid], A[hi]])\n", + " if s[1] == A[low]:\n", + " return low\n", + " elif s[1] == A[mid]:\n", + " return mid\n", + " return hi\n", + " \n", + "def partition(A, low, hi):\n", + " pivotIndex = get_pivot(A, low, hi)\n", + " pivotValue = A[pivotIndex]\n", + " A[pivotIndex], A[low] = A[low], A[pivotIndex]\n", + " border = low\n", + "\n", + " for i in range(low, hi+1):\n", + " if A[i] < pivotValue:\n", + " border += 1\n", + " A[i], A[border] = A[border], A[i]\n", + " A[low], A[border] = A[border], A[low]\n", + "\n", + " return (border)\n", + " \n", + "def quick_selection(x, first, last):\n", + " for i in range (first, last):\n", + " minIndex = i\n", + " for j in range (i+1, last+1):\n", + " if x[j] < x[minIndex]:\n", + " minIndex = j\n", + " if minIndex != i:\n", + " x[i], x[minIndex] = x[minIndex], x[i]\n", + " \n", + "A = [5,9,1,2,4,8,6,3,7]\n", + "print(A)\n", + "quick_sort(A)\n", + "print(A)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Nice simple version written by Mr. UncleChu in comments\n", + "Slick code, but does not sort in place, so uses a lot more memory. Do not use for large lists or you'll get stackoverflow." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[5, 9, 1, 2, 4, 8, 6, 3, 7]\n", + "[1, 2, 3, 4, 5, 6, 7, 8, 9]\n" + ] + } + ], + "source": [ + "def quick_sort_chu(a_list):\n", + " if len(a_list) < 2: return a_list\n", + " lesser = quick_sort([x for x in a_list[1:] if x <= a_list[0]])\n", + " bigger = quick_sort([x for x in a_list[1:] if x > a_list[0]])\n", + " return sum([lesser, [a_list[0]], bigger], [])\n", + "A = [5,9,1,2,4,8,6,3,7]\n", + "print(A)\n", + "B = quick_sort_chu(A)\n", + "print(B)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "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": "3.7.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From a52176b2bbc4ed5ab2ae644c545d6ed4869eddce Mon Sep 17 00:00:00 2001 From: Joe James Date: Tue, 9 Jun 2020 13:41:55 -0700 Subject: [PATCH 10/53] Add Pandas IO and Time Series notebooks --- Pandas/Python Pandas Input-Output.ipynb | 762 ++++++++++++++++ Pandas/Python Pandas Time Series Data.ipynb | 962 ++++++++++++++++++++ 2 files changed, 1724 insertions(+) create mode 100644 Pandas/Python Pandas Input-Output.ipynb create mode 100644 Pandas/Python Pandas Time Series Data.ipynb diff --git a/Pandas/Python Pandas Input-Output.ipynb b/Pandas/Python Pandas Input-Output.ipynb new file mode 100644 index 00000000..4fc3f862 --- /dev/null +++ b/Pandas/Python Pandas Input-Output.ipynb @@ -0,0 +1,762 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Python Pandas I/O\n", + "### Creating DataFrames, Reading and Writing to CSV & JSON files \n", + "[Documentation](https://pandas.pydata.org/docs/index.html)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import random" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Creating DataFrames from Lists and Dicts\n", + "▶ New DataFrame from a **List** \n", + "Pandas automatically assigns numerical row indexes." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Ht Len Wt\n", + "0 63 45.0 2.6\n", + "1 29 NaN 1.7\n", + "2 37 71.0 4.2" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data4 = pd.read_json('outjson.txt')\n", + "data4" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "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": "3.7.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Pandas/Python Pandas Time Series Data.ipynb b/Pandas/Python Pandas Time Series Data.ipynb new file mode 100644 index 00000000..1152cd3e --- /dev/null +++ b/Pandas/Python Pandas Time Series Data.ipynb @@ -0,0 +1,962 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Python Pandas Time Series Data\n", + "[Documentation](https://pandas.pydata.org/docs/user_guide/timeseries.html)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import datetime\n", + "import random" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Data Formats Supported\n", + "Pandas datetime64 can interpret strings, Python datetime, and Numpy datetime64 objects. \n", + "Also note, a list of pd.datetime64 objects are automatically converted to a DatetimeIndex." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "DatetimeIndex(['2020-06-01', '2020-06-02', '2020-06-03', '2020-06-04',\n", + " '2020-06-05'],\n", + " dtype='datetime64[ns]', freq=None)" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a1 = pd.to_datetime([\n", + " '6/1/2020', \n", + " '6-2-2020',\n", + " datetime.datetime(2020, 6, 3),\n", + " np.datetime64('2020-06-04'),\n", + " np.datetime64('2020-06-05')])\n", + "a1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Pass in a format argument for custom formatted dates (case matters)." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "DatetimeIndex(['2020-06-14', '2020-06-15'], dtype='datetime64[ns]', freq=None)" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a2 = pd.to_datetime(['2020/14/06', '2020/15/06'], format='%Y/%d/%m')\n", + "a2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Hours and Minutes too? No problem." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "DatetimeIndex(['2020-08-06 14:05:00', '2020-09-06 06:45:00'], dtype='datetime64[ns]', freq=None)" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a3 = pd.to_datetime(\n", + " ['2020/6/8 14.05', '2020/6/9 06.45'], format='%Y/%d/%m %H.%M')\n", + "a3" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Creating a datetime sequence with fixed intervals\n", + "freq parameters: \n", + " D=days, W=weeks, M=months, B=business days, BW=bus weeks, BM=bus months" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "DatetimeIndex(['2020-06-01', '2020-06-02', '2020-06-03', '2020-06-04',\n", + " '2020-06-05', '2020-06-06', '2020-06-07', '2020-06-08',\n", + " '2020-06-09', '2020-06-10', '2020-06-11', '2020-06-12',\n", + " '2020-06-13', '2020-06-14', '2020-06-15', '2020-06-16',\n", + " '2020-06-17', '2020-06-18', '2020-06-19', '2020-06-20',\n", + " '2020-06-21', '2020-06-22', '2020-06-23', '2020-06-24',\n", + " '2020-06-25', '2020-06-26', '2020-06-27', '2020-06-28',\n", + " '2020-06-29', '2020-06-30'],\n", + " dtype='datetime64[ns]', freq='D')\n" + ] + }, + { + "data": { + "text/html": [ + "
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alphabeta
2020-05-2910.4026
2020-05-308.9226
2020-05-315.0912
2020-06-013.8727
2020-06-023.9324
2020-06-034.7916
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" + ], + "text/plain": [ + " alpha beta\n", + "2020-05-29 10.40 26\n", + "2020-05-30 8.92 26\n", + "2020-05-31 5.09 12\n", + "2020-06-01 3.87 27\n", + "2020-06-02 3.93 24\n", + "2020-06-03 4.79 16\n", + "2020-06-04 9.12 16" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "d1 = np.round(6 + 4 * np.random.randn(7), decimals=2)\n", + "d2 = np.random.randint(12, 30, size=7)\n", + "d3 = pd.Series(pd.date_range('2020-05-29', periods=7, freq='1d'))\n", + "df = pd.DataFrame({'alpha':d1, 'beta':d2}, index=d3)\n", + "\n", + "df.to_csv('file01.csv')\n", + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n" + ] + }, + { + "data": { + "text/html": [ + "
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alphabeta
2020-05-2910.4026
2020-05-308.9226
2020-05-315.0912
\n", + "
" + ], + "text/plain": [ + " alpha beta\n", + "2020-05-29 10.40 26\n", + "2020-05-30 8.92 26\n", + "2020-05-31 5.09 12" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = pd.read_csv('file01.csv', index_col=0)\n", + "print(type(df.index[2]))\n", + "df.index = pd.to_datetime(df.index, format='%Y/%m/%d')\n", + "print(type(df.index[2]))\n", + "df.loc[:'2020/05/31']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Constructing Dates from Multiple Columns\n", + "You have Month, Day and Year in separate fields, and need to combine them into a single Datetime field." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1998 10 10 0.4173998814595933\n" + ] + } + ], + "source": [ + "yyyy = [random.randint(1995,2020) for i in range(100)]\n", + "mm = [random.randint(1,12) for i in range(100)]\n", + "dd = [random.randint(1,28) for i in range(100)]\n", + "data = [random.random() for i in range(100)]\n", + "print(yyyy[5], mm[5], dd[5], data[5])" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 1998-10-10 00:00:00\n", + "1 0.4174\n", + "Name: 5, dtype: object" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df1 = pd.DataFrame({'year': yyyy,'month': mm, 'day': dd})\n", + "df1 = pd.to_datetime(df1) \n", + "df2 = pd.Series(data)\n", + "df = pd.concat([df1, df2], axis=1)\n", + "df.loc[5]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Date Arithmetic" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'Thursday'" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "appointment = pd.Timestamp('2020-06-04')\n", + "appointment.day_name()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Uh oh! my appointment is delayed 2 days. \n", + "Here are 3 different ways to add 2 days to the date." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'Saturday'" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "appointment = pd.Timestamp('2020-06-04')\n", + "appointment += pd.Timedelta('2 days')\n", + "appointment.day_name()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'Saturday'" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "appointment = pd.Timestamp('2020-06-04')\n", + "appointment += pd.Timedelta(days=2)\n", + "appointment.day_name()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Date offsets: Day, Hour, Minute, Second, Milli, Micro, Nano " + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'Saturday'" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "appointment = pd.Timestamp('2020-06-04')\n", + "appointment += pd.offsets.Day(2)\n", + "appointment.day_name()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "NO, it's delayed 2 business days. " + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'Monday'" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "appointment = pd.Timestamp('2020-06-04')\n", + "appointment += pd.offsets.BDay(2)\n", + "appointment.day_name()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "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": "3.7.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From c48285101b74831e4e2cc61f17623e454a58db99 Mon Sep 17 00:00:00 2001 From: Joe James Date: Fri, 12 Jun 2020 09:59:37 -0700 Subject: [PATCH 11/53] Adding Data files --- Pandas/Python Pandas Time Series Data.ipynb | 424 ++++++++------------ Pandas/file01.csv | 8 + Pandas/outfile.csv | 4 + Pandas/outjson.txt | 1 + Pandas/pivot.csv | 2 + 5 files changed, 174 insertions(+), 265 deletions(-) create mode 100644 Pandas/file01.csv create mode 100644 Pandas/outfile.csv create mode 100644 Pandas/outjson.txt create mode 100644 Pandas/pivot.csv diff --git a/Pandas/Python Pandas Time Series Data.ipynb b/Pandas/Python Pandas Time Series Data.ipynb index 1152cd3e..3db1b4b4 100644 --- a/Pandas/Python Pandas Time Series Data.ipynb +++ b/Pandas/Python Pandas Time Series Data.ipynb @@ -217,7 +217,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 61, "metadata": {}, "outputs": [ { @@ -246,212 +246,16 @@ " \n", " \n", " \n", - " 2020-06-07\n", - " 0.816153\n", - " \n", - " \n", - " 2020-06-14\n", - " 0.142715\n", - " \n", - " \n", - " 2020-06-21\n", - " 0.883367\n", - " \n", - " \n", - " 2020-06-28\n", - " 0.042243\n", - " \n", - " \n", - " 2020-07-05\n", - " 0.902994\n", - " \n", - " \n", " 2020-07-12\n", - " 0.122903\n", + " 0.581691\n", " \n", " \n", " 2020-07-19\n", - " 0.883686\n", + " 0.611492\n", " \n", " \n", " 2020-07-26\n", - " 0.599879\n", - " \n", - " \n", - " 2020-08-02\n", - " 0.585071\n", - " \n", - " \n", - " 2020-08-09\n", - " 0.487260\n", - " \n", - " \n", - " 2020-08-16\n", - " 0.577816\n", - " \n", - " \n", - " 2020-08-23\n", - " 0.217873\n", - " \n", - " \n", - " 2020-08-30\n", - " 0.642893\n", - " \n", - " \n", - " 2020-09-06\n", - " 0.269524\n", - " \n", - " \n", - " 2020-09-13\n", - " 0.681086\n", - " \n", - " \n", - " 2020-09-20\n", - " 0.066728\n", - " \n", - " \n", - " 2020-09-27\n", - " 0.192068\n", - " \n", - " \n", - " 2020-10-04\n", - " 0.430084\n", - " \n", - " \n", - " 2020-10-11\n", - " 0.673437\n", - " \n", - " \n", - " 2020-10-18\n", - " 0.216691\n", - " \n", - " \n", - " 2020-10-25\n", - " 0.172302\n", - " \n", - " \n", - " 2020-11-01\n", - " 0.034461\n", - " \n", - " \n", - " 2020-11-08\n", - " 0.764077\n", - " \n", - " \n", - " 2020-11-15\n", - " 0.166790\n", - " \n", - " \n", - " 2020-11-22\n", - " 0.379369\n", - " \n", - " \n", - " 2020-11-29\n", - " 0.026259\n", - " \n", - " \n", - " 2020-12-06\n", - " 0.215560\n", - " \n", - " \n", - " 2020-12-13\n", - " 0.912649\n", - " \n", - " \n", - " 2020-12-20\n", - " 0.752434\n", - " \n", - " \n", - " 2020-12-27\n", - " 0.129831\n", - " \n", - " \n", - " 2021-01-03\n", - " 0.307117\n", - " \n", - " \n", - " 2021-01-10\n", - " 0.119653\n", - " \n", - " \n", - " 2021-01-17\n", - " 0.385201\n", - " \n", - " \n", - " 2021-01-24\n", - " 0.219277\n", - " \n", - " \n", - " 2021-01-31\n", - " 0.962418\n", - " \n", - " \n", - " 2021-02-07\n", - " 0.265013\n", - " \n", - " \n", - " 2021-02-14\n", - " 0.178213\n", - " \n", - " \n", - " 2021-02-21\n", - " 0.833785\n", - " \n", - " \n", - " 2021-02-28\n", - " 0.668348\n", - " \n", - " \n", - " 2021-03-07\n", - " 0.826616\n", - " \n", - " \n", - " 2021-03-14\n", - " 0.345981\n", - " \n", - " \n", - " 2021-03-21\n", - " 0.619586\n", - " \n", - " \n", - " 2021-03-28\n", - " 0.362562\n", - " \n", - " \n", - " 2021-04-04\n", - " 0.765329\n", - " \n", - " \n", - " 2021-04-11\n", - " 0.800720\n", - " \n", - " \n", - " 2021-04-18\n", - " 0.156365\n", - " \n", - " \n", - " 2021-04-25\n", - " 0.988019\n", - " \n", - " \n", - " 2021-05-02\n", - " 0.587013\n", - " \n", - " \n", - " 2021-05-09\n", - " 0.709290\n", - " \n", - " \n", - " 2021-05-16\n", - " 0.862771\n", - " \n", - " \n", - " 2021-05-23\n", - " 0.475769\n", - " \n", - " \n", - " 2021-05-30\n", - " 0.860615\n", + " 0.933940\n", " \n", " \n", "\n", @@ -459,61 +263,12 @@ ], "text/plain": [ " 0\n", - 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"2021-02-28 0.668348\n", - "2021-03-07 0.826616\n", - "2021-03-14 0.345981\n", - "2021-03-21 0.619586\n", - "2021-03-28 0.362562\n", - "2021-04-04 0.765329\n", - "2021-04-11 0.800720\n", - "2021-04-18 0.156365\n", - "2021-04-25 0.988019\n", - "2021-05-02 0.587013\n", - "2021-05-09 0.709290\n", - "2021-05-16 0.862771\n", - "2021-05-23 0.475769\n", - "2021-05-30 0.860615" + "2020-07-12 0.581691\n", + "2020-07-19 0.611492\n", + "2020-07-26 0.933940" ] }, - "execution_count": 6, + "execution_count": 61, "metadata": {}, "output_type": "execute_result" } @@ -522,7 +277,7 @@ "b3 = np.random.rand(52)\n", "b4 = pd.date_range('2020-06-01', periods=52, freq='W')\n", "df = pd.DataFrame(b3, index=b4)\n", - "df" + "df['2020-07-10':'2020-07-28']" ] }, { @@ -661,7 +416,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 63, "metadata": {}, "outputs": [ { @@ -724,7 +479,7 @@ "2020-05-31 5.09 12" ] }, - "execution_count": 9, + "execution_count": 63, "metadata": {}, "output_type": "execute_result" } @@ -734,7 +489,7 @@ "print(type(df.index[2]))\n", "df.index = pd.to_datetime(df.index, format='%Y/%m/%d')\n", "print(type(df.index[2]))\n", - "df.loc[:'2020/05/31']" + "df[:'2020/05/31']" ] }, { @@ -747,14 +502,14 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 25, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "1998 10 10 0.4173998814595933\n" + "2017 12 7 0.970109923902562\n" ] } ], @@ -768,18 +523,74 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 64, "metadata": {}, "outputs": [ { "data": { + "text/html": [ + "
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02016-10-180.282307
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" + ], "text/plain": [ - "0 1998-10-10 00:00:00\n", - "1 0.4174\n", - "Name: 5, dtype: object" + " 0 1\n", + "0 2016-10-18 0.282307\n", + "1 2007-09-09 0.004984\n", + "2 2016-12-12 0.652762\n", + "3 2017-04-14 0.199284\n", + "4 2013-03-23 0.163154" ] }, - "execution_count": 11, + "execution_count": 64, "metadata": {}, "output_type": "execute_result" } @@ -789,7 +600,90 @@ "df1 = pd.to_datetime(df1) \n", "df2 = pd.Series(data)\n", "df = pd.concat([df1, df2], axis=1)\n", - "df.loc[5]" + "df[:5]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Pivot (Transpose) Rows & Columns\n", + "You normally want dates as the row index, not the column headers. \n", + "Flip the rows and columns using T." + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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--- /dev/null +++ b/Pandas/outjson.txt @@ -0,0 +1 @@ +{"0":{"Ht":63,"Len":45.0,"Wt":2.6},"1":{"Ht":29,"Len":null,"Wt":1.7},"2":{"Ht":37,"Len":71.0,"Wt":4.2}} \ No newline at end of file diff --git a/Pandas/pivot.csv b/Pandas/pivot.csv new file mode 100644 index 00000000..0c603dc6 --- /dev/null +++ b/Pandas/pivot.csv @@ -0,0 +1,2 @@ +2016-10-18 00:00:00,2007-09-09 00:00:00,2016-12-12 00:00:00,2017-04-14 00:00:00,2013-03-23 00:00:00,2017-12-07 00:00:00,2008-06-05 00:00:00,2004-12-06 00:00:00,1995-11-05 00:00:00,1996-09-12 00:00:00,2001-05-23 00:00:00,1997-07-08 00:00:00,1995-05-01 00:00:00,2008-11-06 00:00:00,2020-12-07 00:00:00,1998-02-03 00:00:00,1996-12-20 00:00:00,1998-04-25 00:00:00,2019-03-09 00:00:00,2019-08-25 00:00:00,2015-12-01 00:00:00,2004-04-08 00:00:00,2015-04-19 00:00:00,2013-12-23 00:00:00,2008-07-17 00:00:00,2016-02-16 00:00:00,2004-05-08 00:00:00,2000-10-26 00:00:00,1999-04-27 00:00:00,2014-06-23 00:00:00,2014-04-02 00:00:00,1999-06-05 00:00:00,1998-10-20 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From 0db1d49f4a1f55d50107ecc8f988679c87eb2c0d Mon Sep 17 00:00:00 2001 From: Joe James Date: Thu, 18 Jun 2020 12:54:45 -0700 Subject: [PATCH 12/53] Add files via upload --- Pandas/Pandas - Change Column Names.ipynb | 364 +++++++++ ...ndas - Delete Columns from DataFrame.ipynb | 757 ++++++++++++++++++ ...Pandas - Iterate Rows of a DataFrame.ipynb | 723 +++++++++++++++++ Pandas/iris.data | 151 ++++ 4 files changed, 1995 insertions(+) create mode 100644 Pandas/Pandas - Change Column Names.ipynb create mode 100644 Pandas/Pandas - Delete Columns from DataFrame.ipynb create mode 100644 Pandas/Pandas - Iterate Rows of a DataFrame.ipynb create mode 100644 Pandas/iris.data diff --git a/Pandas/Pandas - Change Column Names.ipynb b/Pandas/Pandas - Change Column Names.ipynb new file mode 100644 index 00000000..aa84e79c --- /dev/null +++ b/Pandas/Pandas - Change Column Names.ipynb @@ -0,0 +1,364 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Pandas - Change Column Names" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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45.03.61.40.2Iris-setosa
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45.03.61.40.2Iris-setosa
..................
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01234
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[x for x in df[2]]\n", + "col2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Iterate Rows with Index and each Row as a List\n", + "**DO NOT** try to change data in the df this way, but it is convenient for iterating. \n", + "Itertuples is supposed to be much faster than Iterrows for large datasets." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 5.1 3.5 1.4 0.2 Iris-setosa\n", + "1 4.9 3.0 1.4 0.2 Iris-setosa\n", + "2 4.7 3.2 1.3 0.2 Iris-setosa\n", + "3 4.6 3.1 1.5 0.2 Iris-setosa\n", + "4 5.0 3.6 1.4 0.2 Iris-setosa\n", + "5 5.4 3.9 1.7 0.4 Iris-setosa\n", + "6 4.6 3.4 1.4 0.3 Iris-setosa\n", + "7 5.0 3.4 1.5 0.2 Iris-setosa\n", + "8 4.4 2.9 1.4 0.2 Iris-setosa\n", + "9 4.9 3.1 1.5 0.1 Iris-setosa\n", + "10 5.4 3.7 1.5 0.2 Iris-setosa\n", + "11 4.8 3.4 1.6 0.2 Iris-setosa\n", + "12 4.8 3.0 1.4 0.1 Iris-setosa\n", + "13 4.3 3.0 1.1 0.1 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6.1 2.3 Iris-virginica\n", + "136 6.3 3.4 5.6 2.4 Iris-virginica\n", + "137 6.4 3.1 5.5 1.8 Iris-virginica\n", + "138 6.0 3.0 4.8 1.8 Iris-virginica\n", + "139 6.9 3.1 5.4 2.1 Iris-virginica\n", + "140 6.7 3.1 5.6 2.4 Iris-virginica\n", + "141 6.9 3.1 5.1 2.3 Iris-virginica\n", + "142 5.8 2.7 5.1 1.9 Iris-virginica\n", + "143 6.8 3.2 5.9 2.3 Iris-virginica\n", + "144 6.7 3.3 5.7 2.5 Iris-virginica\n", + "145 6.7 3.0 5.2 2.3 Iris-virginica\n", + "146 6.3 2.5 5.0 1.9 Iris-virginica\n", + "147 6.5 3.0 5.2 2.0 Iris-virginica\n", + "148 6.2 3.4 5.4 2.3 Iris-virginica\n", + "149 5.9 3.0 5.1 1.8 Iris-virginica\n" + ] + } + ], + "source": [ + "for i, row in df.iterrows():\n", + " print(i, row[0], row[1], row[2], row[3], row[4])" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 5.1 3.5 1.4 0.2 Iris-setosa\n", + "1 4.9 3.0 1.4 0.2 Iris-setosa\n", + "2 4.7 3.2 1.3 0.2 Iris-setosa\n", + "3 4.6 3.1 1.5 0.2 Iris-setosa\n", + "4 5.0 3.6 1.4 0.2 Iris-setosa\n", + "5 5.4 3.9 1.7 0.4 Iris-setosa\n", + "6 4.6 3.4 1.4 0.3 Iris-setosa\n", + "7 5.0 3.4 1.5 0.2 Iris-setosa\n", + "8 4.4 2.9 1.4 0.2 Iris-setosa\n", + "9 4.9 3.1 1.5 0.1 Iris-setosa\n", + "10 5.4 3.7 1.5 0.2 Iris-setosa\n", + "11 4.8 3.4 1.6 0.2 Iris-setosa\n", + "12 4.8 3.0 1.4 0.1 Iris-setosa\n", + "13 4.3 3.0 1.1 0.1 Iris-setosa\n", + "14 5.8 4.0 1.2 0.2 Iris-setosa\n", + "15 5.7 4.4 1.5 0.4 Iris-setosa\n", + "16 5.4 3.9 1.3 0.4 Iris-setosa\n", + "17 5.1 3.5 1.4 0.3 Iris-setosa\n", + "18 5.7 3.8 1.7 0.3 Iris-setosa\n", + "19 5.1 3.8 1.5 0.3 Iris-setosa\n", + "20 5.4 3.4 1.7 0.2 Iris-setosa\n", + "21 5.1 3.7 1.5 0.4 Iris-setosa\n", + "22 4.6 3.6 1.0 0.2 Iris-setosa\n", + "23 5.1 3.3 1.7 0.5 Iris-setosa\n", + "24 4.8 3.4 1.9 0.2 Iris-setosa\n", + "25 5.0 3.0 1.6 0.2 Iris-setosa\n", + "26 5.0 3.4 1.6 0.4 Iris-setosa\n", + "27 5.2 3.5 1.5 0.2 Iris-setosa\n", + "28 5.2 3.4 1.4 0.2 Iris-setosa\n", + "29 4.7 3.2 1.6 0.2 Iris-setosa\n", + "30 4.8 3.1 1.6 0.2 Iris-setosa\n", + "31 5.4 3.4 1.5 0.4 Iris-setosa\n", + "32 5.2 4.1 1.5 0.1 Iris-setosa\n", + "33 5.5 4.2 1.4 0.2 Iris-setosa\n", + "34 4.9 3.1 1.5 0.1 Iris-setosa\n", + "35 5.0 3.2 1.2 0.2 Iris-setosa\n", + "36 5.5 3.5 1.3 0.2 Iris-setosa\n", + "37 4.9 3.1 1.5 0.1 Iris-setosa\n", + "38 4.4 3.0 1.3 0.2 Iris-setosa\n", + "39 5.1 3.4 1.5 0.2 Iris-setosa\n", + "40 5.0 3.5 1.3 0.3 Iris-setosa\n", + "41 4.5 2.3 1.3 0.3 Iris-setosa\n", + "42 4.4 3.2 1.3 0.2 Iris-setosa\n", + "43 5.0 3.5 1.6 0.6 Iris-setosa\n", + "44 5.1 3.8 1.9 0.4 Iris-setosa\n", + "45 4.8 3.0 1.4 0.3 Iris-setosa\n", + "46 5.1 3.8 1.6 0.2 Iris-setosa\n", + "47 4.6 3.2 1.4 0.2 Iris-setosa\n", + "48 5.3 3.7 1.5 0.2 Iris-setosa\n", + "49 5.0 3.3 1.4 0.2 Iris-setosa\n", + "50 7.0 3.2 4.7 1.4 Iris-versicolor\n", + "51 6.4 3.2 4.5 1.5 Iris-versicolor\n", + "52 6.9 3.1 4.9 1.5 Iris-versicolor\n", + "53 5.5 2.3 4.0 1.3 Iris-versicolor\n", + "54 6.5 2.8 4.6 1.5 Iris-versicolor\n", + "55 5.7 2.8 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Iris-virginica\n", + "103 6.3 2.9 5.6 1.8 Iris-virginica\n", + "104 6.5 3.0 5.8 2.2 Iris-virginica\n", + "105 7.6 3.0 6.6 2.1 Iris-virginica\n", + "106 4.9 2.5 4.5 1.7 Iris-virginica\n", + "107 7.3 2.9 6.3 1.8 Iris-virginica\n", + "108 6.7 2.5 5.8 1.8 Iris-virginica\n", + "109 7.2 3.6 6.1 2.5 Iris-virginica\n", + "110 6.5 3.2 5.1 2.0 Iris-virginica\n", + "111 6.4 2.7 5.3 1.9 Iris-virginica\n", + "112 6.8 3.0 5.5 2.1 Iris-virginica\n", + "113 5.7 2.5 5.0 2.0 Iris-virginica\n", + "114 5.8 2.8 5.1 2.4 Iris-virginica\n", + "115 6.4 3.2 5.3 2.3 Iris-virginica\n", + "116 6.5 3.0 5.5 1.8 Iris-virginica\n", + "117 7.7 3.8 6.7 2.2 Iris-virginica\n", + "118 7.7 2.6 6.9 2.3 Iris-virginica\n", + "119 6.0 2.2 5.0 1.5 Iris-virginica\n", + "120 6.9 3.2 5.7 2.3 Iris-virginica\n", + "121 5.6 2.8 4.9 2.0 Iris-virginica\n", + "122 7.7 2.8 6.7 2.0 Iris-virginica\n", + "123 6.3 2.7 4.9 1.8 Iris-virginica\n", + "124 6.7 3.3 5.7 2.1 Iris-virginica\n", + "125 7.2 3.2 6.0 1.8 Iris-virginica\n", + "126 6.2 2.8 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+ ], + "source": [ + "for row in df.itertuples(name=None): \n", + " print(row[0], row[1], row[2], row[3], row[4], row[5])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "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": "3.7.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Pandas/iris.data b/Pandas/iris.data new file mode 100644 index 00000000..5c4316cd --- /dev/null +++ b/Pandas/iris.data @@ -0,0 +1,151 @@ +5.1,3.5,1.4,0.2,Iris-setosa +4.9,3.0,1.4,0.2,Iris-setosa +4.7,3.2,1.3,0.2,Iris-setosa +4.6,3.1,1.5,0.2,Iris-setosa +5.0,3.6,1.4,0.2,Iris-setosa +5.4,3.9,1.7,0.4,Iris-setosa 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1466.32.55.01.9Iris-virginica
1476.53.05.22.0Iris-virginica
1486.23.45.42.3Iris-virginica
1495.93.05.11.8Iris-virginica
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" + ], + "text/plain": [ + " A B C D Label\n", + "0 5.1 3.5 1.4 0.2 Iris-setosa\n", + "1 4.9 3.0 1.4 0.2 Iris-setosa\n", + "2 4.7 3.2 1.3 0.2 Iris-setosa\n", + "3 4.6 3.1 1.5 0.2 Iris-setosa\n", + "4 5.0 3.6 1.4 0.2 Iris-setosa\n", + ".. ... ... ... ... ...\n", + "145 6.7 3.0 5.2 2.3 Iris-virginica\n", + "146 6.3 2.5 5.0 1.9 Iris-virginica\n", + "147 6.5 3.0 5.2 2.0 Iris-virginica\n", + "148 6.2 3.4 5.4 2.3 Iris-virginica\n", + "149 5.9 3.0 5.1 1.8 Iris-virginica\n", + "\n", + "[150 rows x 5 columns]" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = pd.read_csv('iris.data', names=['A','B','C','D','Label'])\n", + "df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "1) get a list of the column names." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['A', 'B', 'C', 'D', 'Label']" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "titles = list(df.columns)\n", + "titles" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "2) Swap or move whatever columns you want in the list." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['A', 'C', 'B', 'D', 'Label']" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "titles[1], titles[2] = titles[2], titles[1]\n", + "titles" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "3) Reassign the columns in the DataFrame." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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ACBDLabel
05.11.43.50.2Iris-setosa
14.91.43.00.2Iris-setosa
24.71.33.20.2Iris-setosa
34.61.53.10.2Iris-setosa
45.01.43.60.2Iris-setosa
..................
1456.75.23.02.3Iris-virginica
1466.35.02.51.9Iris-virginica
1476.55.23.02.0Iris-virginica
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150 rows × 5 columns

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" + ], + "text/plain": [ + " A C B D Label\n", + "0 5.1 1.4 3.5 0.2 Iris-setosa\n", + "1 4.9 1.4 3.0 0.2 Iris-setosa\n", + "2 4.7 1.3 3.2 0.2 Iris-setosa\n", + "3 4.6 1.5 3.1 0.2 Iris-setosa\n", + "4 5.0 1.4 3.6 0.2 Iris-setosa\n", + ".. ... ... ... ... ...\n", + "145 6.7 5.2 3.0 2.3 Iris-virginica\n", + "146 6.3 5.0 2.5 1.9 Iris-virginica\n", + "147 6.5 5.2 3.0 2.0 Iris-virginica\n", + "148 6.2 5.4 3.4 2.3 Iris-virginica\n", + "149 5.9 5.1 3.0 1.8 Iris-virginica\n", + "\n", + "[150 rows x 5 columns]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = df[titles]\n", + "df" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "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": "3.7.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 4ed5a68b4dd4d0ed50476d8c001f2a345fb6de38 Mon Sep 17 00:00:00 2001 From: saksham044 <72141820+saksham044@users.noreply.github.com> Date: Wed, 30 Sep 2020 18:40:38 +0530 Subject: [PATCH 14/53] Update .gitattributes --- .gitattributes | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.gitattributes b/.gitattributes index bdb0cabc..cee6d0d8 100644 --- a/.gitattributes +++ b/.gitattributes @@ -1,7 +1,7 @@ -# Auto detect text files and perform LF normalization +# Auto detect the text files and perform LF normalization easily! * text=auto -# Custom for Visual Studio +# Custom for Visual Studio (Any Version) *.cs diff=csharp # Standard to msysgit From 5bd1af68d3d47649459a45a13ecc17ad9d00aff0 Mon Sep 17 00:00:00 2001 From: Ashish0931 <40832481+Ashish0931@users.noreply.github.com> Date: Wed, 30 Sep 2020 21:02:12 +0530 Subject: [PATCH 15/53] Update Pandas - Delete Columns from DataFrame.ipynb --- Pandas/Pandas - Delete Columns from DataFrame.ipynb | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/Pandas/Pandas - Delete Columns from DataFrame.ipynb b/Pandas/Pandas - Delete Columns from DataFrame.ipynb index e728b3bf..28293203 100644 --- a/Pandas/Pandas - Delete Columns from DataFrame.ipynb +++ b/Pandas/Pandas - Delete Columns from DataFrame.ipynb @@ -174,8 +174,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### 1) to drop a single column\n", - "df.drop('col_name', axis=1) \n", + "### 1) to drop a single column\n, and saving it permanently ", + "df.drop('col_name', axis=1,inplace =True) \n", "To save changes you must either set df = df.drop(), or add inplace=True." ] }, From 00e9b3859533f2702dc6b38c6322c60907493dc3 Mon Sep 17 00:00:00 2001 From: Jayant123-Joker <72143741+Jayant123-Joker@users.noreply.github.com> Date: Wed, 30 Sep 2020 21:31:01 +0530 Subject: [PATCH 16/53] Update graph_adjacency-matrix.py --- graph_adjacency-matrix.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/graph_adjacency-matrix.py b/graph_adjacency-matrix.py index b6d05589..3f315001 100644 --- a/graph_adjacency-matrix.py +++ b/graph_adjacency-matrix.py @@ -1,4 +1,5 @@ # implementation of an undirected graph using Adjacency Matrix, with weighted or unweighted edges +# its definitely work class Vertex: def __init__(self, n): self.name = n @@ -46,4 +47,4 @@ def print_graph(self): for edge in edges: g.add_edge(edge[:1], edge[1:]) -g.print_graph() \ No newline at end of file +g.print_graph() From 62d2bc8d665bcfbf101227817b5910db14fee486 Mon Sep 17 00:00:00 2001 From: Parmodsihag <68704187+Parmodsihag@users.noreply.github.com> Date: Wed, 30 Sep 2020 21:53:30 +0530 Subject: [PATCH 17/53] added else statement with try except also added some more explanation --- exception-handling.py | 37 ++++++++++++++++++++++++++++++++++++- 1 file changed, 36 insertions(+), 1 deletion(-) diff --git a/exception-handling.py b/exception-handling.py index 8dd489f1..57ddf118 100644 --- a/exception-handling.py +++ b/exception-handling.py @@ -1,3 +1,20 @@ +# something more about try except +# basic syntax +''' +try: + code1 + +except: + some code that will execute if code 1 fails or raise some error + +else: + this code is executed only if try was succesful i.e no error in code1 + +finally: + + this code will execute in every situation if try fails or not +''' + filename = 'exception_data.txt' # Outer try block catches file name or file doesn't exist errors. try: @@ -28,4 +45,22 @@ def this_fails(): try: this_fails() except ZeroDivisionError as err: - print('Handling run-time error:', err) \ No newline at end of file + print('Handling run-time error:', err) + + +def divide_me(n): + x = 1/n + +i = int(input('enter a number ')) +try: + divide_me(i) + +except Exception as e: + print(e) # this will print the error msg but don't kill the execution of program + +else: + print('Your Code Run Successfully') # this will execute if divide_me(i) run sucessfully without an error + +finally: + print('thanks') # this will execute in every condition + From 8c6e715df625ca125a365f7fcfe976d515a24c36 Mon Sep 17 00:00:00 2001 From: OpensourceContributor07 <69769312+OpensourceContributor07@users.noreply.github.com> Date: Thu, 1 Oct 2020 00:12:30 +0530 Subject: [PATCH 18/53] Update README.md --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index 6f0d92be..f0c8d233 100644 --- a/README.md +++ b/README.md @@ -5,6 +5,7 @@ and are mainly intended for educational purposes. You are invited to subscribe to my video channel, and to download and use any code in this Python repository, according to the MIT License. Feel free to post any comments on my YouTube channel. +i am very happy to see you there on my you tube channel.excited!!!!!!!!! Joe James. Fremont, California. From 1c8e553935e7d13301f9de44ca18e4c0721752bd Mon Sep 17 00:00:00 2001 From: SaiSuvamPatnaik <63333249+SaiSuvamPatnaik@users.noreply.github.com> Date: Thu, 1 Oct 2020 01:10:13 +0530 Subject: [PATCH 19/53] Update README.md --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index 6f0d92be..cbfbb2e0 100644 --- a/README.md +++ b/README.md @@ -9,3 +9,4 @@ Feel free to post any comments on my YouTube channel. Joe James. Fremont, California. Copyright (C) 2015-2019, Joe James +HELLO From 85c2146e1c595f4c19352b8144736290ae3853a7 Mon Sep 17 00:00:00 2001 From: JAI ARORA <64232138+JAI-ARORA@users.noreply.github.com> Date: Thu, 1 Oct 2020 03:08:35 +0530 Subject: [PATCH 20/53] Update factorial.py --- factorial.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/factorial.py b/factorial.py index 2a70c3dc..8e4a65ea 100644 --- a/factorial.py +++ b/factorial.py @@ -14,6 +14,6 @@ def get_iterative_factorial(n): for i in range(1, n+1): fact *= i return fact - +print("input should be an integer") print(get_recursive_factorial(6)) -print(get_iterative_factorial(6)) \ No newline at end of file +print(get_iterative_factorial(6)) From 78ae3b4aa8de92e77af540f2efa8af89e6fba8ae Mon Sep 17 00:00:00 2001 From: anay2310 <70628601+anay2310@users.noreply.github.com> Date: Thu, 1 Oct 2020 09:07:07 +0530 Subject: [PATCH 21/53] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 6f0d92be..760428bf 100644 --- a/README.md +++ b/README.md @@ -1,4 +1,4 @@ -# Python +# Python 3 These files are mainly intended to accompany my series of YouTube tutorial videos here, https://www.youtube.com/user/joejamesusa and are mainly intended for educational purposes. From 3677438558ebf2af5cef2fc7fb20f2dbef2b481f Mon Sep 17 00:00:00 2001 From: AmanKumar05032005 <72191898+AmanKumar05032005@users.noreply.github.com> Date: Thu, 1 Oct 2020 14:26:15 +0530 Subject: [PATCH 22/53] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 6f0d92be..a3544ea3 100644 --- a/README.md +++ b/README.md @@ -2,7 +2,7 @@ These files are mainly intended to accompany my series of YouTube tutorial videos here, https://www.youtube.com/user/joejamesusa and are mainly intended for educational purposes. -You are invited to subscribe to my video channel, and to download and use any code in +You are invited to subscribe to my video channel-Joe James, and to download and use any code in this Python repository, according to the MIT License. Feel free to post any comments on my YouTube channel. From dc5c7aaf735b6656c0ce24206e7958569623f4bc Mon Sep 17 00:00:00 2001 From: Aniket Gupta <63919139+aniketguptaa@users.noreply.github.com> Date: Thu, 1 Oct 2020 15:20:20 +0530 Subject: [PATCH 23/53] Update README.md --- README.md | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/README.md b/README.md index 6f0d92be..b8ce25de 100644 --- a/README.md +++ b/README.md @@ -6,6 +6,10 @@ You are invited to subscribe to my video channel, and to download and use any co this Python repository, according to the MIT License. Feel free to post any comments on my YouTube channel. + +This source codes are easy to understand and reliable for self study and you will learn them easily, try to practice more coding by making algorithms yourself and you can be better python programmer and remember "Try to learn something about everything and everything about something". + +Thank you for reviewing my repositories and keep practising. Joe James. Fremont, California. Copyright (C) 2015-2019, Joe James From 33182dd11d1c66a4a46071a650851ebb16179f74 Mon Sep 17 00:00:00 2001 From: beingritik <72251017+beingritik@users.noreply.github.com> Date: Fri, 2 Oct 2020 14:16:14 +0530 Subject: [PATCH 24/53] change the information --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 6f0d92be..9f0a1d95 100644 --- a/README.md +++ b/README.md @@ -7,5 +7,5 @@ this Python repository, according to the MIT License. Feel free to post any comments on my YouTube channel. Joe James. -Fremont, California. +Fremont, CA. Copyright (C) 2015-2019, Joe James From 67ed35cf3cc1755ba09649fff201b8d85884b750 Mon Sep 17 00:00:00 2001 From: Aiswarya29 <72244069+Aiswarya29@users.noreply.github.com> Date: Fri, 2 Oct 2020 17:47:45 +0530 Subject: [PATCH 25/53] Create Heapsort.py --- Sorting Algorithms/Heapsort.py | 33 +++++++++++++++++++++++++++++++++ 1 file changed, 33 insertions(+) create mode 100644 Sorting Algorithms/Heapsort.py diff --git a/Sorting Algorithms/Heapsort.py b/Sorting Algorithms/Heapsort.py new file mode 100644 index 00000000..9a2e2c14 --- /dev/null +++ b/Sorting Algorithms/Heapsort.py @@ -0,0 +1,33 @@ +# heapify +def heapify(arr, n, i): + largest = i # largest value + l = 2 * i + 1 # left + r = 2 * i + 2 # right + # if left child exists + if l < n and arr[i] < arr[l]: + largest = l + # if right child exits + if r < n and arr[largest] < arr[r]: + largest = r + # root + if largest != i: + arr[i],arr[largest] = arr[largest],arr[i] # swap + # root. + heapify(arr, n, largest) +# sort +def heapSort(arr): + n = len(arr) + # maxheap + for i in range(n, -1, -1): + heapify(arr, n, i) + # element extraction + for i in range(n-1, 0, -1): + arr[i], arr[0] = arr[0], arr[i] # swap + heapify(arr, i, 0) +# main +arr = [2,5,3,8,6,5,4,7] +heapSort(arr) +n = len(arr) +print ("Sorted array is") +for i in range(n): + print (arr[i],end=" ") From 6f4622e530df36920567565380582d37e26bcd75 Mon Sep 17 00:00:00 2001 From: Ethan Palani <46998848+Ethan0507@users.noreply.github.com> Date: Fri, 2 Oct 2020 21:23:00 +0530 Subject: [PATCH 26/53] Update CircularLinkedList.py Overwriting the inbuilt '__str__' method to print the Node, rather than creating a new method and calling it. --- LinkedLists/CircularLinkedList.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/LinkedLists/CircularLinkedList.py b/LinkedLists/CircularLinkedList.py index b6733be7..48a5a9df 100644 --- a/LinkedLists/CircularLinkedList.py +++ b/LinkedLists/CircularLinkedList.py @@ -16,7 +16,7 @@ def get_data (self): def set_data (self, d): self.data = d - def to_string (self): + def __str__(self): return "Node value: " + str(self.data) class CircularLinkedList (object): @@ -71,10 +71,10 @@ def print_list (self): if self.root is None: return this_node = self.root - print (this_node.to_string()) + print (this_node) while this_node.get_next() != self.root: this_node = this_node.get_next() - print (this_node.to_string()) + print (this_node) def main(): myList = CircularLinkedList() @@ -87,10 +87,10 @@ def main(): print("Find 12", myList.find(12)) cur = myList.root - print (cur.to_string()) + print (cur) for i in range(8): cur = cur.get_next(); - print (cur.to_string()) + print (cur) print("size="+str(myList.get_size())) myList.print_list() From b02dd159c8d711fd862f6abf5a2fdc4b5f972686 Mon Sep 17 00:00:00 2001 From: Govindrajewar <72288656+Govindrajewar@users.noreply.github.com> Date: Sat, 3 Oct 2020 09:00:21 +0530 Subject: [PATCH 27/53] Create python oriented programming This is all Program which I run in python --- python oriented programming | 34 ++++++++++++++++++++++++++++++++++ 1 file changed, 34 insertions(+) create mode 100644 python oriented programming diff --git a/python oriented programming b/python oriented programming new file mode 100644 index 00000000..8e3a4499 --- /dev/null +++ b/python oriented programming @@ -0,0 +1,34 @@ +class Mobile: + def make_call(self): + print("i am making a call") + def play_game(self): + print("i am playing games") + +m1=Mobile() + +m1.make_call() + +m1.play_game() + +class Mobile: + def set_color(self,color): + self.color=color + def set_cost(self,cost): + self.cost=cost + def show_color(self): + print("black") + def show_price(self): + print("5000") + def make_call(self): + print("i am making a call") + def play_game(self): + print("i am playing games") + + + +m2=Mobile() + +m2.show_price() + +m2.show_color() + From b46ea8082cf485c60519cd5f73578bba1c59a2ce Mon Sep 17 00:00:00 2001 From: PRAJESH <47695045+Prajesh-Srivastava@users.noreply.github.com> Date: Sun, 4 Oct 2020 08:18:29 +0530 Subject: [PATCH 28/53] Create Some Basic Terminologies in Python Data Structure --- ...sic Terminologies in Python Data Structure | 31 +++++++++++++++++++ 1 file changed, 31 insertions(+) create mode 100644 Intro to Python Data Structures/Some Basic Terminologies in Python Data Structure diff --git a/Intro to Python Data Structures/Some Basic Terminologies in Python Data Structure b/Intro to Python Data Structures/Some Basic Terminologies in Python Data Structure new file mode 100644 index 00000000..754c07a5 --- /dev/null +++ b/Intro to Python Data Structures/Some Basic Terminologies in Python Data Structure @@ -0,0 +1,31 @@ +##Data Structure Overview +Data structures are fundamental concepts of computer science which helps is writing efficient programs in any language. Python is a high-level, interpreted, interactive and object-oriented scripting language using which we can study the fundamentals of data structure in a simpler way as compared to other programming languages. + +In this chapter we are going to study a short overview of some frequently used data structures in general and how they are related to some specific python data types. There are also some data structures specific to python which is listed as another category. + +##General Data Structures +The various data structures in computer science are divided broadly into two categories shown below. We will discuss about each of the below data structures in detail in subsequent chapters. + +#Liner Data Structures +These are the data structures which store the data elements in a sequential manner. + +Array: It is a sequential arrangement of data elements paired with the index of the data element. +Linked List: Each data element contains a link to another element along with the data present in it. +Stack: It is a data structure which follows only to specific order of operation. LIFO(last in First Out) or FILO(First in Last Out). +Queue: It is similar to Stack but the order of operation is only FIFO(First In First Out). +Matrix: It is two dimensional data structure in which the data element is referred by a pair of indices. + +#Non-Liner Data Structures +These are the data structures in which there is no sequential linking of data elements. Any pair or group of data elements can be linked to each other and can be accessed without a strict sequence. + +Binary Tree: It is a data structure where each data element can be connected to maximum two other data elements and it starts with a root node. +Heap: It is a special case of Tree data structure where the data in the parent node is either strictly greater than/ equal to the child nodes or strictly less than it’s child nodes. +Hash Table: It is a data structure which is made of arrays associated with each other using a hash function. It retrieves values using keys rather than index from a data element. +Graph: .It is an arrangement of vertices and nodes where some of the nodes are connected to each other through links. + +#Python Specific Data Structures +These data structures are specific to python language and they give greater flexibility in storing different types of data and faster processing in python environment. + +List: It is similar to array with the exception that the data elements can be of different data types. You can have both numeric and string data in a python list. +Tuple: Tuples are similar to lists but they are immutable which means the values in a tuple cannot be modified they can only be read. +Dictionary: The dictionary contains Key-value pairs as its data elements. From 42ff89e35a280ef9f8e6e5ff049081a36212a39b Mon Sep 17 00:00:00 2001 From: Ritik <63651014+ritik1234@users.noreply.github.com> Date: Mon, 5 Oct 2020 09:02:43 +0530 Subject: [PATCH 29/53] just made the content easier to read for the user. (#45) --- Python List Iteration.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Python List Iteration.ipynb b/Python List Iteration.ipynb index d680f54f..7361caaf 100644 --- a/Python List Iteration.ipynb +++ b/Python List Iteration.ipynb @@ -13,7 +13,7 @@ "metadata": {}, "source": [ "----\n", - "The standard for loop works great if inside the loop you only need the item and not its index." + "The standard for loop works well if it is used inside the loop you only need the item and not its index." ] }, { From 49f05ba131f7525f24208c8138b8351875928f37 Mon Sep 17 00:00:00 2001 From: sid2631 <69759968+sid2631@users.noreply.github.com> Date: Mon, 5 Oct 2020 15:33:43 +0530 Subject: [PATCH 30/53] Create addition of two number addition of two number --- addition of two number | 9 +++++++++ 1 file changed, 9 insertions(+) create mode 100644 addition of two number diff --git a/addition of two number b/addition of two number new file mode 100644 index 00000000..d31335e3 --- /dev/null +++ b/addition of two number @@ -0,0 +1,9 @@ +# Store input numbers +num1 = input('Enter first number: ') +num2 = input('Enter second number: ') + +# Add two numbers +sum = float(num1) + float(num2) + +# Display the sum +print('The sum of {0} and {1} is {2}'.format(num1, num2, sum)) From d0ebf11ae6223e2259dd471d52c41af4f8a5c648 Mon Sep 17 00:00:00 2001 From: PRAJESH <47695045+Prajesh-Srivastava@users.noreply.github.com> Date: Thu, 8 Oct 2020 09:39:20 +0530 Subject: [PATCH 31/53] Update Date_Time_Timestamp.py --- Date Time Timestamp/Date_Time_Timestamp.py | 30 +++++++++++----------- 1 file changed, 15 insertions(+), 15 deletions(-) diff --git a/Date Time Timestamp/Date_Time_Timestamp.py b/Date Time Timestamp/Date_Time_Timestamp.py index 7897332c..cc533dcf 100644 --- a/Date Time Timestamp/Date_Time_Timestamp.py +++ b/Date Time Timestamp/Date_Time_Timestamp.py @@ -19,21 +19,21 @@ # get day of the week using date.weekday() # Monday is 0 from datetime import date -d1 = date.today() -print(d1) -print(d1.month, d1.day, d1.year) -print(d1.weekday()) +todays_date = date.today() +print(todays_date) +print(todays_date.month, todays_date.day, todays_date.year) +print(todays_date.weekday()) # ISO format is a string format, yyyy-mm-dd # --------------------------- # date_object.isoformat() does the same thing as str(date_object) from datetime import date -d1 = date.fromisoformat('2011-11-23') -print(d1) -print(str(d1)) -print(d1.isoformat()) -d1 +todays_date = date.fromisoformat('2011-11-23') +print(todays_date) +print(str(todays_date)) +print(todays_date.isoformat()) +todays_date # Comparison, addition and sutraction of dates # --------------------------- @@ -42,10 +42,10 @@ # The same comparison and add/subtract operations can be used with time objects. from datetime import date -d1 = date.today() +todays_date = date.today() d2 = date(2015, 5, 14) -print(d1 > d2) -print(d1 - d2) +print(todays_date > d2) +print(todays_date - d2) # Time # --------------------------- @@ -95,9 +95,9 @@ # A timedelta can also be multiplied or divided by an integer or float from datetime import timedelta, date, time -d1 = date(2011, 6, 15) +todays_date = date(2011, 6, 15) d2 = date(2012, 9, 18) -td = d2 - d1 +td = d2 - todays_date print(td, type(td)) print(td.total_seconds()) print(td * 3) @@ -130,4 +130,4 @@ start_time = time.process_time() # do some stuff end_time = time.process_time() -print('operation executed in ', end_time - start_time) \ No newline at end of file +print('operation executed in ', end_time - start_time) From 9aa8a9a31cd5e33dda4150736a84658515d55187 Mon Sep 17 00:00:00 2001 From: Sh1710 <72331454+Sh1710@users.noreply.github.com> Date: Thu, 8 Oct 2020 11:56:34 +0530 Subject: [PATCH 32/53] Update README.md --- README.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 5dc9c6ad..22d802cb 100644 --- a/README.md +++ b/README.md @@ -6,7 +6,7 @@ You are invited to subscribe to my video channel-Joe James, and to download and this Python repository, according to the MIT License. Feel free to post any comments on my YouTube channel. i am very happy to see you there on my you tube channel.excited!!!!!!!!! - +## Subscribe my channel for more tutorial videos. This source codes are easy to understand and reliable for self study and you will learn them easily, try to practice more coding by making algorithms yourself and you can be better python programmer and remember "Try to learn something about everything and everything about something". @@ -15,3 +15,4 @@ Joe James. Fremont, CA. Copyright (C) 2015-2019, Joe James HELLO +## Happy coding guys!😀 From 576c4ead4535179969a5daca7e54234e41d0c8f2 Mon Sep 17 00:00:00 2001 From: Joe James Date: Fri, 9 Oct 2020 21:50:51 -0700 Subject: [PATCH 33/53] Update README.md --- README.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/README.md b/README.md index 22d802cb..3c0ced97 100644 --- a/README.md +++ b/README.md @@ -5,14 +5,14 @@ and are mainly intended for educational purposes. You are invited to subscribe to my video channel-Joe James, and to download and use any code in this Python repository, according to the MIT License. Feel free to post any comments on my YouTube channel. -i am very happy to see you there on my you tube channel.excited!!!!!!!!! -## Subscribe my channel for more tutorial videos. +i am very happy to see you there on my you tube channel. excited!!!!!!!!! +## Subscribe to my channel for more tutorial videos. This source codes are easy to understand and reliable for self study and you will learn them easily, try to practice more coding by making algorithms yourself and you can be better python programmer and remember "Try to learn something about everything and everything about something". -Thank you for reviewing my repositories and keep practising. +Thank you for reviewing my repositories and keep practicing. Joe James. Fremont, CA. -Copyright (C) 2015-2019, Joe James -HELLO +Copyright (C) 2015-2020, Joe James + ## Happy coding guys!😀 From 56045c1a7ecd4579e51aed358861abbaa7a8dee9 Mon Sep 17 00:00:00 2001 From: gopesanjay772 <72152228+gopesanjay772@users.noreply.github.com> Date: Sat, 17 Oct 2020 10:03:45 +0530 Subject: [PATCH 34/53] Update lcm.py --- lcm.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/lcm.py b/lcm.py index 8d584ab7..a308141e 100644 --- a/lcm.py +++ b/lcm.py @@ -1,4 +1,4 @@ -# computes Lowest Common Multiple LCM / Least Common Denominator LCD +# computes Lowest Common Multiple (LCM) / Least Common Denominator (LCD) # useful for adding and subtracting fractions # 2 numbers @@ -21,4 +21,4 @@ def lcm3(nums): print(str(lcm(7, 12))) nums = [3, 2, 16] -print(str(lcm3(nums))) \ No newline at end of file +print(str(lcm3(nums))) From e7f9b1ad920e3ec4eb2945fbc98d22de298b7d8b Mon Sep 17 00:00:00 2001 From: Aniket Gupta <63919139+aniketguptaa@users.noreply.github.com> Date: Sun, 18 Oct 2020 00:32:44 +0530 Subject: [PATCH 35/53] Update Queues implementaion.py --- Queues implementaion.py | 9 --------- 1 file changed, 9 deletions(-) diff --git a/Queues implementaion.py b/Queues implementaion.py index da9bad1a..63170c8c 100644 --- a/Queues implementaion.py +++ b/Queues implementaion.py @@ -54,15 +54,6 @@ def display(self): for i in self.items: ar.append(i) return ar - - - - - - - - - que = Queue() que.enqueue('google') que.enqueue('youtube') From 6c2897775348d3151bc1d87dd1c10bdd9ec29a20 Mon Sep 17 00:00:00 2001 From: princerajputana6 <37314665+princerajputana6@users.noreply.github.com> Date: Wed, 21 Oct 2020 03:07:55 +0530 Subject: [PATCH 36/53] Update README.md --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index 3c0ced97..c520b85f 100644 --- a/README.md +++ b/README.md @@ -1,3 +1,4 @@ +#This is a open source project. # Python 3 These files are mainly intended to accompany my series of YouTube tutorial videos here, https://www.youtube.com/user/joejamesusa From ad6cab029a28aac7e1c2fbd6fb7e773e10390a97 Mon Sep 17 00:00:00 2001 From: Joe James Date: Wed, 21 Oct 2020 11:48:34 -0700 Subject: [PATCH 37/53] Add files via upload --- Sorting Algorithms/Radix_Sort.ipynb | 113 ++++++++++++++++++++++++++++ 1 file changed, 113 insertions(+) create mode 100644 Sorting Algorithms/Radix_Sort.ipynb diff --git a/Sorting Algorithms/Radix_Sort.ipynb b/Sorting Algorithms/Radix_Sort.ipynb new file mode 100644 index 00000000..4862a2e5 --- /dev/null +++ b/Sorting Algorithms/Radix_Sort.ipynb @@ -0,0 +1,113 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Radix Sort\n", + "(c) 2020, Joe James" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# get number of digits in largest item\n", + "def __get_num_digits(A):\n", + " m = 0\n", + " for item in A:\n", + " m = max(m, item)\n", + " return len(str(m))" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# flatten into a 1D List\n", + "from functools import reduce\n", + "def __flatten(A):\n", + " return reduce(lambda x, y: x + y, A)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "def radix(A, num_digits):\n", + " for digit in range(0, num_digits):\n", + " B = [[] for i in range(10)]\n", + " for item in A:\n", + " # num is the bucket number that the item will be put into\n", + " num = item // 10 ** (digit) % 10\n", + " B[num].append(item)\n", + " A = __flatten(B)\n", + " return A" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1, 2, 3, 45, 53, 55, 213, 288, 289]\n", + "[0, 1, 2, 3, 4, 5] [999994, 999995, 999996, 999997, 999998, 999999]\n" + ] + } + ], + "source": [ + "def main():\n", + " A = [55, 45, 3, 289, 213, 1, 288, 53, 2]\n", + " num_digits = __get_num_digits(A)\n", + " A = radix(A, num_digits)\n", + " print(A)\n", + " \n", + " B = [i for i in range(1000000)]\n", + " from random import shuffle\n", + " shuffle(B)\n", + " B = radix(B, __get_num_digits(B))\n", + " print(B[:6], B[-6:])\n", + "\n", + "main()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "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": "3.7.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From f97b3a239189d7c66bb019790e65a0b5ff879f30 Mon Sep 17 00:00:00 2001 From: Joe James Date: Wed, 21 Oct 2020 19:57:42 -0700 Subject: [PATCH 38/53] Revised function call. Moved __get_num_digits() function call into the radix() function, so the only parameter when calling radix() is a List. --- Sorting Algorithms/Radix_Sort.ipynb | 24 ++++++++++++++++-------- 1 file changed, 16 insertions(+), 8 deletions(-) diff --git a/Sorting Algorithms/Radix_Sort.ipynb b/Sorting Algorithms/Radix_Sort.ipynb index 4862a2e5..0b701528 100644 --- a/Sorting Algorithms/Radix_Sort.ipynb +++ b/Sorting Algorithms/Radix_Sort.ipynb @@ -10,7 +10,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -24,7 +24,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -34,13 +34,22 @@ " return reduce(lambda x, y: x + y, A)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Changed from YouTube video:\n", + "It's much cleaner to put the _get_num_digits call inside the radix function rather than in main as shown in the video. That way you only need to pass a List to the radix function. Thanks to Brother Lui for this suggestion." + ] + }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ - "def radix(A, num_digits):\n", + "def radix(A):\n", + " num_digits = __get_num_digits(A)\n", " for digit in range(0, num_digits):\n", " B = [[] for i in range(10)]\n", " for item in A:\n", @@ -53,7 +62,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -68,14 +77,13 @@ "source": [ "def main():\n", " A = [55, 45, 3, 289, 213, 1, 288, 53, 2]\n", - " num_digits = __get_num_digits(A)\n", - " A = radix(A, num_digits)\n", + " A = radix(A)\n", " print(A)\n", " \n", " B = [i for i in range(1000000)]\n", " from random import shuffle\n", " shuffle(B)\n", - " B = radix(B, __get_num_digits(B))\n", + " B = radix(B)\n", " print(B[:6], B[-6:])\n", "\n", "main()" From f7893b62e479182590acd4fcdb7af277099e47b9 Mon Sep 17 00:00:00 2001 From: Varun Shrivastava Date: Sat, 27 Feb 2021 20:28:14 +0530 Subject: [PATCH 39/53] Added a new .py file for classes in python(OOP) --- .../building our first class.py | 20 +++++++++++++++++++ 1 file changed, 20 insertions(+) create mode 100644 Object Oriented Programming/building our first class.py diff --git a/Object Oriented Programming/building our first class.py b/Object Oriented Programming/building our first class.py new file mode 100644 index 00000000..51411d5e --- /dev/null +++ b/Object Oriented Programming/building our first class.py @@ -0,0 +1,20 @@ +#Today we will learn how to create a class and other attributes of class +#Below is the method how classes are defined +class Student: + pass + +#Below is the method to create object , Here Varun and rohan are two objects of Class Student +Varun = Student() +larry = Student() + +# Now after creating objects we can use them to call variables +Varun.name = "Harry" +Varun.std = 12 +Varun.section = 1 +larry.std = 9 +larry.subjects = ["hindi", "physics"] +print(Varun.section, larry.subjects) + + + + From 779b27f990a0e21ce6ede0f49b4cd1228ae5f73d Mon Sep 17 00:00:00 2001 From: Varun Shrivastava Date: Mon, 1 Mar 2021 19:41:36 +0530 Subject: [PATCH 40/53] Added contributing file --- Contributing.txt | 19 +++++++++++++++++++ 1 file changed, 19 insertions(+) create mode 100644 Contributing.txt diff --git a/Contributing.txt b/Contributing.txt new file mode 100644 index 00000000..50913f5c --- /dev/null +++ b/Contributing.txt @@ -0,0 +1,19 @@ +Contributions are always welcome!!!! +If you want to contribute to this repository follow the below procedure - +1. Fork this repository +2. Clone the code to your local system and go through readme.md +3. You can create another branch to add further commits + +GIT COMMANDS FOR CONTRIBUTING - +1. To clone this repository +`git clone [code link]` +2.To create new branch +`git checkout -b [branch name] ` +3. To stage files +`git add .` +4.To commit changes +`git commit -m "commit message"` +5. To push changes +`git push [remote branch] [new branch]` + + \ No newline at end of file From 02d3f3dd02cc3041bbf2bafd284503deb92397b7 Mon Sep 17 00:00:00 2001 From: Varun Shrivastava Date: Tue, 2 Mar 2021 19:31:29 +0530 Subject: [PATCH 41/53] updated Contributing.txt --- Contributing.txt | 2 ++ 1 file changed, 2 insertions(+) diff --git a/Contributing.txt b/Contributing.txt index 50913f5c..52d1fd31 100644 --- a/Contributing.txt +++ b/Contributing.txt @@ -16,4 +16,6 @@ GIT COMMANDS FOR CONTRIBUTING - 5. To push changes `git push [remote branch] [new branch]` +HAPPY CONTRIBUTION!!!!!!!! + \ No newline at end of file From 4b7f7777518d20309c9f0dd64aa7f0b20451f58b Mon Sep 17 00:00:00 2001 From: Joe James Date: Wed, 13 Oct 2021 15:28:41 -0700 Subject: [PATCH 42/53] Added Python 10 match statements notebook --- match statements.ipynb | 222 +++++++++++++++++++++++++++++++++++++++++ 1 file changed, 222 insertions(+) create mode 100644 match statements.ipynb diff --git a/match statements.ipynb b/match statements.ipynb new file mode 100644 index 00000000..2bc9b2dd --- /dev/null +++ b/match statements.ipynb @@ -0,0 +1,222 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Python 10 - Structural Pattern Matching\n", + "### match statements \n", + "Very similar to switch/case statements in C, Java, and Javascript. \n", + "Can be used in lieu of if/elif/else blocks. \n", + "[documentation](https://www.python.org/dev/peps/pep-0622/)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "large\n" + ] + } + ], + "source": [ + "var = 3\n", + "\n", + "match var:\n", + " case 1:\n", + " print('small')\n", + " case 2:\n", + " print('medium')\n", + " case 3:\n", + " print('large')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### The Default case _ \n", + "The default case, using underscore, is optional. " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "large\n" + ] + } + ], + "source": [ + "var = 4\n", + "\n", + "match var:\n", + " case 1:\n", + " print('small')\n", + " case 2:\n", + " print('medium')\n", + " case _:\n", + " print('large')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Conditionals in case \n", + "if statements and or (using bar) are supported in case statements." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "small\n" + ] + } + ], + "source": [ + "var = 2\n", + "\n", + "match var:\n", + " case x if x<=3:\n", + " print('small')\n", + " case 4 | 5 | 6:\n", + " print('medium')\n", + " case _:\n", + " print('large')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### No breaks needed\n", + "Note that you do not need break statements. The match block will automatically end execution after one case is executed." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "A\n", + "F\n" + ] + } + ], + "source": [ + "def print_grade(score):\n", + " match score:\n", + " # case score > 90 this does not work!\n", + " case score if score >= 90:\n", + " print('A')\n", + " case score if score >= 80:\n", + " print('B')\n", + " case score if score >= 70:\n", + " print('C')\n", + " case score if score >= 60:\n", + " print('D')\n", + " case _:\n", + " print('F')\n", + " \n", + "print_grade(94)\n", + "print_grade(48)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Python Objects \n", + "match statements can also use Python objects and instance variables." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "welcome to the business program!\n", + "welcome to the science program!\n" + ] + } + ], + "source": [ + "class Student:\n", + " def __init__(self, n, i, m):\n", + " self.name = n\n", + " self.id = i\n", + " self.major = m\n", + "\n", + "def welcome(student):\n", + " match student.major:\n", + " case 'engineering':\n", + " print('welcome to the engineering program!')\n", + " case 'business':\n", + " print('welcome to the business program!')\n", + " case 'pharmacy':\n", + " print('welcome to the pharmacy program!')\n", + " case x:\n", + " print(f'welcome to the {x} program!')\n", + " \n", + "new_student = Student('Suresh', 5723, 'business')\n", + "welcome(new_student)\n", + "\n", + "new_student = Student('Britney', 5724, 'science')\n", + "welcome(new_student)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.10.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 71cc10b4e2063d6b8811e5f16004e207ecfd28c4 Mon Sep 17 00:00:00 2001 From: Joe James Date: Wed, 13 Oct 2021 15:31:36 -0700 Subject: [PATCH 43/53] Update README.md --- README.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index c520b85f..939de1ed 100644 --- a/README.md +++ b/README.md @@ -6,14 +6,14 @@ and are mainly intended for educational purposes. You are invited to subscribe to my video channel-Joe James, and to download and use any code in this Python repository, according to the MIT License. Feel free to post any comments on my YouTube channel. -i am very happy to see you there on my you tube channel. excited!!!!!!!!! +I am very happy to see you there on my you tube channel. excited!!!!!!!!! ## Subscribe to my channel for more tutorial videos. -This source codes are easy to understand and reliable for self study and you will learn them easily, try to practice more coding by making algorithms yourself and you can be better python programmer and remember "Try to learn something about everything and everything about something". +This source code is easy to understand and reliable for self study and you will learn them easily, try to practice more coding by making algorithms yourself and you can become a better Python programmer, and remember "Try to learn something about everything and everything about something". Thank you for reviewing my repositories and keep practicing. Joe James. Fremont, CA. -Copyright (C) 2015-2020, Joe James +Copyright (C) 2015-2021, Joe James ## Happy coding guys!😀 From e897fb6703f9672bf3aa353ed7f490f1e5591717 Mon Sep 17 00:00:00 2001 From: Joe James Date: Fri, 15 Oct 2021 21:06:47 -0700 Subject: [PATCH 44/53] updated match-case notebook --- match statements.ipynb | 154 ++++++++++++++++++++++++++++++++++------- 1 file changed, 128 insertions(+), 26 deletions(-) diff --git a/match statements.ipynb b/match statements.ipynb index 2bc9b2dd..eaccdc9f 100644 --- a/match statements.ipynb +++ b/match statements.ipynb @@ -11,6 +11,13 @@ "[documentation](https://www.python.org/dev/peps/pep-0622/)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Can use integer for match variable..." + ] + }, { "cell_type": "code", "execution_count": 1, @@ -36,6 +43,103 @@ " print('large')" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### ...or floating point..." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "large\n" + ] + } + ], + "source": [ + "var = 1.5\n", + "\n", + "match var:\n", + " case 1.3:\n", + " print('small')\n", + " case 1.4:\n", + " print('medium')\n", + " case 1.5:\n", + " print('large')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### ...or Tuple...\n", + "Note here we also use a variable to receive *any* value." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "on x-axis\n" + ] + } + ], + "source": [ + "var = (8,0)\n", + "\n", + "match var:\n", + " case (0,x):\n", + " print('on y-axis')\n", + " case (x,0):\n", + " print('on x-axis')\n", + " case (x,y):\n", + " print('not on axis')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### ...or String" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "small\n" + ] + } + ], + "source": [ + "var = \"S\"\n", + "\n", + "match var:\n", + " case \"S\":\n", + " print('small')\n", + " case \"Med\":\n", + " print('medium')\n", + " case \"Lg\":\n", + " print('large')" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -46,7 +150,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -74,12 +178,12 @@ "metadata": {}, "source": [ "#### Conditionals in case \n", - "if statements and or (using bar) are supported in case statements." + "*if* statements and *or* (using bar) are supported in case statements." ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -112,7 +216,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -153,41 +257,39 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "welcome to the business program!\n", - "welcome to the science program!\n" + "medium\n", + "Size XL is not recognized.\n" ] } ], "source": [ - "class Student:\n", - " def __init__(self, n, i, m):\n", - " self.name = n\n", - " self.id = i\n", - " self.major = m\n", + "class T_shirt:\n", + " def __init__(self, s):\n", + " self.size = s\n", "\n", - "def welcome(student):\n", - " match student.major:\n", - " case 'engineering':\n", - " print('welcome to the engineering program!')\n", - " case 'business':\n", - " print('welcome to the business program!')\n", - " case 'pharmacy':\n", - " print('welcome to the pharmacy program!')\n", - " case x:\n", - " print(f'welcome to the {x} program!')\n", + " def order(self):\n", + " match self.size:\n", + " case 'S' | 'Sm':\n", + " print('small')\n", + " case 'M' | 'Med':\n", + " print('medium')\n", + " case 'L' | 'Lg':\n", + " print('large')\n", + " case x:\n", + " print(f'Size {x} is not recognized.')\n", " \n", - "new_student = Student('Suresh', 5723, 'business')\n", - "welcome(new_student)\n", + "shirt1 = T_shirt('Med')\n", + "shirt1.order()\n", "\n", - "new_student = Student('Britney', 5724, 'science')\n", - "welcome(new_student)" + "shirt2 = T_shirt('XL')\n", + "shirt2.order()" ] }, { From 500c2099fa3a4c679b225656baadc0cf861db58e Mon Sep 17 00:00:00 2001 From: Joe James Date: Sun, 17 Oct 2021 17:34:13 -0700 Subject: [PATCH 45/53] 2nd update to match statements notebook --- match statements.ipynb | 9 ++++++--- 1 file changed, 6 insertions(+), 3 deletions(-) diff --git a/match statements.ipynb b/match statements.ipynb index eaccdc9f..a8fc422d 100644 --- a/match statements.ipynb +++ b/match statements.ipynb @@ -178,7 +178,7 @@ "metadata": {}, "source": [ "#### Conditionals in case \n", - "*if* statements and *or* (using bar) are supported in case statements." + "*or* conditions (using bar) are supported in case statements." ] }, { @@ -198,7 +198,7 @@ "var = 2\n", "\n", "match var:\n", - " case x if x<=3:\n", + " case 2 | 3:\n", " print('small')\n", " case 4 | 5 | 6:\n", " print('medium')\n", @@ -211,6 +211,8 @@ "metadata": {}, "source": [ "#### No breaks needed\n", + "*if* statements are supported, but must follow syntax, case var if (inequality expression). \n", + "\n", "Note that you do not need break statements. The match block will automatically end execution after one case is executed." ] }, @@ -252,7 +254,8 @@ "metadata": {}, "source": [ "#### Python Objects \n", - "match statements can also use Python objects and instance variables." + "Match statements can also use Python objects and instance variables. \n", + "In the final case here we could have used _ default case, but instead used x so that we could use the value of x in our print statement." ] }, { From ccb7bdf4b9369e06c4cd5860d368546365836d50 Mon Sep 17 00:00:00 2001 From: Joe James Date: Thu, 28 Oct 2021 17:20:21 -0700 Subject: [PATCH 46/53] Create temp --- Iris Dataset/temp | 1 + 1 file changed, 1 insertion(+) create mode 100644 Iris Dataset/temp diff --git a/Iris Dataset/temp b/Iris Dataset/temp new file mode 100644 index 00000000..9c595a6f --- /dev/null +++ b/Iris Dataset/temp @@ -0,0 +1 @@ +temp From 763ba2befbd41a8c66870c58e3b9b6fe08f72a43 Mon Sep 17 00:00:00 2001 From: Joe James Date: Thu, 28 Oct 2021 17:23:16 -0700 Subject: [PATCH 47/53] Add files via upload --- Iris Dataset/Iris_Dataset.ipynb | 1174 +++++++++++++++++++++++++++++++ Iris Dataset/iris.data | 151 ++++ 2 files changed, 1325 insertions(+) create mode 100644 Iris Dataset/Iris_Dataset.ipynb create mode 100644 Iris Dataset/iris.data diff --git a/Iris Dataset/Iris_Dataset.ipynb b/Iris Dataset/Iris_Dataset.ipynb new file mode 100644 index 00000000..398f454e --- /dev/null +++ b/Iris Dataset/Iris_Dataset.ipynb @@ -0,0 +1,1174 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Data Science\n", + "### Exploring the Iris Dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load Data\n", + "Load the data from CSV file into a Pandas dataframe, and print the top few rows." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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idsepal_lengthsepal_widthpetal_lengthpetal_widthspecies
005.13.51.40.2Iris-setosa
114.93.01.40.2Iris-setosa
224.73.21.30.2Iris-setosa
334.63.11.50.2Iris-setosa
445.03.61.40.2Iris-setosa
\n", + "
" + ], + "text/plain": [ + " id sepal_length sepal_width petal_length petal_width species\n", + "0 0 5.1 3.5 1.4 0.2 Iris-setosa\n", + "1 1 4.9 3.0 1.4 0.2 Iris-setosa\n", + "2 2 4.7 3.2 1.3 0.2 Iris-setosa\n", + "3 3 4.6 3.1 1.5 0.2 Iris-setosa\n", + "4 4 5.0 3.6 1.4 0.2 Iris-setosa" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = pd.read_csv('iris.data')\n", + "data.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Customize columns\n", + "Drop the redundant id column, and rename Attribute columns to integers. Save column names for use later." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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0123species
05.13.51.40.2Iris-setosa
507.03.24.71.4Iris-versicolor
1006.33.36.02.5Iris-virginica
\n", + "
" + ], + "text/plain": [ + " 0 1 2 3 species\n", + "0 5.1 3.5 1.4 0.2 Iris-setosa\n", + "50 7.0 3.2 4.7 1.4 Iris-versicolor\n", + "100 6.3 3.3 6.0 2.5 Iris-virginica" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = data.drop('id', 1)\n", + "cols = ['sepal_length', 'sepal_width', 'petal_length', 'petal_width']\n", + "data.rename(columns = {cols[0]:0, cols[1]:1, cols[2]:2, cols[3]:3}, inplace=True)\n", + "data.loc[::50]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Statistical Overview\n", + "Show shape of dataframe and statistical overview of attribute columns." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(150, 5)\n" + ] + }, + { + "data": { + "text/html": [ + "
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0123
count150.000000150.000000150.000000150.000000
mean5.8433333.0540003.7586671.198667
std0.8280660.4335941.7644200.763161
min4.3000002.0000001.0000000.100000
25%5.1000002.8000001.6000000.300000
50%5.8000003.0000004.3500001.300000
75%6.4000003.3000005.1000001.800000
max7.9000004.4000006.9000002.500000
\n", + "
" + ], + "text/plain": [ + " 0 1 2 3\n", + "count 150.000000 150.000000 150.000000 150.000000\n", + "mean 5.843333 3.054000 3.758667 1.198667\n", + "std 0.828066 0.433594 1.764420 0.763161\n", + "min 4.300000 2.000000 1.000000 0.100000\n", + "25% 5.100000 2.800000 1.600000 0.300000\n", + "50% 5.800000 3.000000 4.350000 1.300000\n", + "75% 6.400000 3.300000 5.100000 1.800000\n", + "max 7.900000 4.400000 6.900000 2.500000" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "print(data.shape)\n", + "data.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Iris-virginica 50\n", + "Iris-setosa 50\n", + "Iris-versicolor 50\n", + "Name: species, dtype: int64" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# same as data['species'].value_counts()\n", + "data.species.value_counts()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Histograms\n", + "Histograms are useful for showing how the data is distributed. They're ridiculously easy to use, but can only show two axes." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([ 9., 23., 14., 27., 16., 26., 18., 6., 5., 6.]),\n", + " array([4.3 , 4.66, 5.02, 5.38, 5.74, 6.1 , 6.46, 6.82, 7.18, 7.54, 7.9 ]),\n", + " )" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(data[0])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here we give 4 columns of data to the Histogram maker, and it automatically color codes them." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([[ 0., 0., 0., 0., 0., 11., 48., 49., 31., 11.],\n", + " [ 0., 0., 11., 97., 38., 4., 0., 0., 0., 0.],\n", + " [ 0., 44., 6., 1., 10., 34., 30., 20., 5., 0.],\n", + " [50., 52., 45., 3., 0., 0., 0., 0., 0., 0.]]),\n", + " array([0.1 , 0.88, 1.66, 2.44, 3.22, 4. , 4.78, 5.56, 6.34, 7.12, 7.9 ]),\n", + " )" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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YPQl8cMR5XqP7S/H9wN+OOstcVbU7yS5gL/AK8DBD3IZgrC+FlCQNZ9yXZSRJQ7DcJalBlrskNchyl6QGWe6S1CDLXZIaZLlLUoP+D78BmV6m0W9NAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist([data[0], data[1], data[2], data[3]])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To add a Legend we need to add labels to the Histogram builder as a list of column names, and call the legend function." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist([data[0], data[1], data[2], data[3]], label=[cols[0],cols[1],cols[2],cols[3]])\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Or we can make 4 separate calls to the Histogram builder and get 4 overlapping plots." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([41., 8., 1., 7., 8., 33., 6., 23., 9., 14.]),\n", + " array([0.1 , 0.34, 0.58, 0.82, 1.06, 1.3 , 1.54, 1.78, 2.02, 2.26, 2.5 ]),\n", + " )" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(data[0])\n", + "plt.hist(data[1]) \n", + "plt.hist(data[2])\n", + "plt.hist(data[3])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can use alpha to control the opacity of plots. alpha of 1 is opague. alpha of 0 is transparent." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([41., 8., 1., 7., 8., 33., 6., 23., 9., 14.]),\n", + " array([0.1 , 0.34, 0.58, 0.82, 1.06, 1.3 , 1.54, 1.78, 2.02, 2.26, 2.5 ]),\n", + " )" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(data[0])\n", + "plt.hist(data[1], alpha=1) \n", + "plt.hist(data[2], alpha=0.6)\n", + "plt.hist(data[3], alpha=0.5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also plot the 4 columns on separate subplots to make it more readable. This is very readable, but beware that each plot automatically scales its axes to the data." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(2, 2, figsize=(8, 4))\n", + "ax[0, 0].hist(data[0])\n", + "ax[0, 1].hist(data[1])\n", + "ax[1, 0].hist(data[2])\n", + "ax[1, 1].hist(data[3])\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Adding titles to the previous plot makes it more readable." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(2, 2, figsize=(8, 6))\n", + "ax[0, 0].hist(data[0])\n", + "ax[0, 1].hist(data[1])\n", + "ax[1, 0].hist(data[2])\n", + "ax[1, 1].hist(data[3])\n", + "ax[0, 0].set_title(cols[0])\n", + "ax[0, 1].set_title(cols[1])\n", + "ax[1, 0].set_title(cols[2])\n", + "ax[1, 1].set_title(cols[3])\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Scatter Plots\n", + "These are probably more useful for this dataset because they can show clusters by species. The most basic scatter plot does not distinguish species." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.scatter(\n", + " data[0], \n", + " data[1],)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Adding color coding by species allows us to see clustering for 2 attributes for each species. Here setosa is secluded, but virginica and versicolor overlap." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "colors = {'Iris-setosa':'red', 'Iris-virginica':'blue', 'Iris-versicolor':'green'}\n", + "plt.scatter(\n", + " data[2], \n", + " data[3], \n", + " c=data['species'].map(colors))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Adding labels to the x and y axes is useful, but we can see the data for virginica and versicolor still overlap. If we could find 1 attribute where there's no overlap for these 2 species then we could use those to definitively distinguish them. But unfortunately all 4 attributes have some overlap." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'petal_length')" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.scatter(\n", + " data[0], \n", + " data[2], \n", + " c=data['species'].map(colors))\n", + "plt.xlabel(cols[0])\n", + "plt.ylabel(cols[2])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here we add a title to the plot, and show attributes 1 and 3. " + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Iris Data Scatter Plot')" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.scatter(\n", + " data[1], \n", + " data[3], \n", + " c=data['species'].map(colors))\n", + "plt.xlabel(cols[1])\n", + "plt.ylabel(cols[3])\n", + "plt.title('Iris Data Scatter Plot')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Correlation\n", + "We can see the correlation between attributes. A correlation close to 1 helps us distinguish between species. Low correlation doesn't help us." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Again, the simplest plots are not very useful, but when we add labels and color coding the plots are revealing." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'whiskers': [,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ],\n", + " 'caps': [,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ],\n", + " 'boxes': [,\n", + " ,\n", + " ,\n", + " ],\n", + " 'medians': [,\n", + " ,\n", + " ,\n", + " ],\n", + " 'fliers': [,\n", + " ,\n", + " ,\n", + " ],\n", + " 'means': []}" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.boxplot([data[0], data[1], data[2], data[3]])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This shows a boxplot for one attribute, sorted by species. For this attribute we can see a big overlap between the 3 species, so it's not very useful for distinguishing. An iris with 5.5 or 6.0 for this attribute could be any of the 3 species." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "data.boxplot(column=[0], by=['species'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It's tricky to do subplots, but worth it. We can see setosa has smaller petals than the other 2 species. And versicolor has, on average, smaller sepals and smaller petals than virginica; but there is some overlap." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'petal_width')" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(2, 2, figsize=(8, 6))\n", + "A = [data[0][data.species == 'Iris-setosa'], data[0][data.species == 'Iris-virginica'], data[0][data.species == 'Iris-versicolor']]\n", + "B = [data[1][data.species == 'Iris-setosa'], data[1][data.species == 'Iris-virginica'], data[1][data.species == 'Iris-versicolor']]\n", + "C = [data[2][data.species == 'Iris-setosa'], data[2][data.species == 'Iris-virginica'], data[2][data.species == 'Iris-versicolor']]\n", + "D = [data[3][data.species == 'Iris-setosa'], data[3][data.species == 'Iris-virginica'], data[3][data.species == 'Iris-versicolor']]\n", + "\n", + "ax[0, 0].boxplot(A, widths = 0.7)\n", + "ax[0, 0].set_title(cols[0])\n", + "ax[0, 1].boxplot(B, widths = 0.7)\n", + "ax[0, 1].set_title(cols[1])\n", + "ax[1, 0].boxplot(C, widths = 0.7)\n", + "ax[1, 0].set_title(cols[2])\n", + "ax[1, 1].boxplot(D, widths = 0.7)\n", + "ax[1, 1].set_title(cols[3])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This plot does an awsome job of showing distributions of all 4 attributes for all 3 species. 12 box plots in 1 graph! The color coding makes it more readable. " + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/ipykernel_launcher.py:21: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance. In a future version, a new instance will always be created and returned. Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "def set_color(bp):\n", + " plt.setp(bp['boxes'][0], color='blue')\n", + " plt.setp(bp['boxes'][1], color='red')\n", + " plt.setp(bp['boxes'][2], color='green')\n", + "\n", + "A = [data[0][data.species == 'Iris-setosa'], data[0][data.species == 'Iris-virginica'], data[0][data.species == 'Iris-versicolor']]\n", + "B = [data[1][data.species == 'Iris-setosa'], data[1][data.species == 'Iris-virginica'], data[1][data.species == 'Iris-versicolor']]\n", + "C = [data[2][data.species == 'Iris-setosa'], data[2][data.species == 'Iris-virginica'], data[2][data.species == 'Iris-versicolor']]\n", + "D = [data[3][data.species == 'Iris-setosa'], data[3][data.species == 'Iris-virginica'], data[3][data.species == 'Iris-versicolor']]\n", + "\n", + "# add this to remove outlier symbols: 0, '',\n", + "bp = plt.boxplot(A, 0, '', positions = [1, 2, 3], widths = 0.7)\n", + "set_color(bp)\n", + "bp = plt.boxplot(B, 0, '', positions = [5, 6, 7], widths = 0.7)\n", + "set_color(bp)\n", + "bp = plt.boxplot(C, 0, '', positions = [9, 10, 11], widths = 0.7)\n", + "set_color(bp)\n", + "bp = plt.boxplot(D, 0, '', positions = [13, 14, 15], widths = 0.7)\n", + "set_color(bp)\n", + "\n", + "ax = plt.axes()\n", + "ax.set_xticks([2, 6, 10, 14])\n", + "ax.set_xticklabels(cols)\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "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": "3.7.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Iris Dataset/iris.data b/Iris Dataset/iris.data new file mode 100644 index 00000000..835f8b44 --- /dev/null +++ b/Iris Dataset/iris.data @@ -0,0 +1,151 @@ +id,sepal_length,sepal_width,petal_length,petal_width,species +0,5.1,3.5,1.4,0.2,Iris-setosa +1,4.9,3.0,1.4,0.2,Iris-setosa +2,4.7,3.2,1.3,0.2,Iris-setosa +3,4.6,3.1,1.5,0.2,Iris-setosa +4,5.0,3.6,1.4,0.2,Iris-setosa +5,5.4,3.9,1.7,0.4,Iris-setosa +6,4.6,3.4,1.4,0.3,Iris-setosa +7,5.0,3.4,1.5,0.2,Iris-setosa +8,4.4,2.9,1.4,0.2,Iris-setosa +9,4.9,3.1,1.5,0.1,Iris-setosa +10,5.4,3.7,1.5,0.2,Iris-setosa +11,4.8,3.4,1.6,0.2,Iris-setosa +12,4.8,3.0,1.4,0.1,Iris-setosa +13,4.3,3.0,1.1,0.1,Iris-setosa +14,5.8,4.0,1.2,0.2,Iris-setosa +15,5.7,4.4,1.5,0.4,Iris-setosa +16,5.4,3.9,1.3,0.4,Iris-setosa +17,5.1,3.5,1.4,0.3,Iris-setosa +18,5.7,3.8,1.7,0.3,Iris-setosa +19,5.1,3.8,1.5,0.3,Iris-setosa +20,5.4,3.4,1.7,0.2,Iris-setosa +21,5.1,3.7,1.5,0.4,Iris-setosa +22,4.6,3.6,1.0,0.2,Iris-setosa +23,5.1,3.3,1.7,0.5,Iris-setosa +24,4.8,3.4,1.9,0.2,Iris-setosa +25,5.0,3.0,1.6,0.2,Iris-setosa 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+56,6.3,3.3,4.7,1.6,Iris-versicolor +57,4.9,2.4,3.3,1.0,Iris-versicolor +58,6.6,2.9,4.6,1.3,Iris-versicolor +59,5.2,2.7,3.9,1.4,Iris-versicolor +60,5.0,2.0,3.5,1.0,Iris-versicolor +61,5.9,3.0,4.2,1.5,Iris-versicolor +62,6.0,2.2,4.0,1.0,Iris-versicolor +63,6.1,2.9,4.7,1.4,Iris-versicolor +64,5.6,2.9,3.6,1.3,Iris-versicolor +65,6.7,3.1,4.4,1.4,Iris-versicolor +66,5.6,3.0,4.5,1.5,Iris-versicolor +67,5.8,2.7,4.1,1.0,Iris-versicolor +68,6.2,2.2,4.5,1.5,Iris-versicolor +69,5.6,2.5,3.9,1.1,Iris-versicolor +70,5.9,3.2,4.8,1.8,Iris-versicolor +71,6.1,2.8,4.0,1.3,Iris-versicolor +72,6.3,2.5,4.9,1.5,Iris-versicolor +73,6.1,2.8,4.7,1.2,Iris-versicolor +74,6.4,2.9,4.3,1.3,Iris-versicolor +75,6.6,3.0,4.4,1.4,Iris-versicolor +76,6.8,2.8,4.8,1.4,Iris-versicolor +77,6.7,3.0,5.0,1.7,Iris-versicolor +78,6.0,2.9,4.5,1.5,Iris-versicolor +79,5.7,2.6,3.5,1.0,Iris-versicolor +80,5.5,2.4,3.8,1.1,Iris-versicolor +81,5.5,2.4,3.7,1.0,Iris-versicolor +82,5.8,2.7,3.9,1.2,Iris-versicolor 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+137,6.4,3.1,5.5,1.8,Iris-virginica +138,6.0,3.0,4.8,1.8,Iris-virginica +139,6.9,3.1,5.4,2.1,Iris-virginica +140,6.7,3.1,5.6,2.4,Iris-virginica +141,6.9,3.1,5.1,2.3,Iris-virginica +142,5.8,2.7,5.1,1.9,Iris-virginica +143,6.8,3.2,5.9,2.3,Iris-virginica +144,6.7,3.3,5.7,2.5,Iris-virginica +145,6.7,3.0,5.2,2.3,Iris-virginica +146,6.3,2.5,5.0,1.9,Iris-virginica +147,6.5,3.0,5.2,2.0,Iris-virginica +148,6.2,3.4,5.4,2.3,Iris-virginica +149,5.9,3.0,5.1,1.8,Iris-virginica From 9823f4ecf5e01874399aaa1cc03b3b958aa9b3b4 Mon Sep 17 00:00:00 2001 From: Joe James Date: Thu, 28 Oct 2021 17:23:40 -0700 Subject: [PATCH 48/53] Delete temp --- Iris Dataset/temp | 1 - 1 file changed, 1 deletion(-) delete mode 100644 Iris Dataset/temp diff --git a/Iris Dataset/temp b/Iris Dataset/temp deleted file mode 100644 index 9c595a6f..00000000 --- a/Iris Dataset/temp +++ /dev/null @@ -1 +0,0 @@ -temp From ca9255abe4f071632a89caaf684719488f3525ff Mon Sep 17 00:00:00 2001 From: Joe James Date: Sat, 6 Nov 2021 13:12:09 -0700 Subject: [PATCH 49/53] Add Iris KNN notebook --- Iris Dataset/KNN-IrisData.ipynb | 629 ++++++++++++++++++++++++++++++++ 1 file changed, 629 insertions(+) create mode 100644 Iris Dataset/KNN-IrisData.ipynb diff --git a/Iris Dataset/KNN-IrisData.ipynb b/Iris Dataset/KNN-IrisData.ipynb new file mode 100644 index 00000000..d49be354 --- /dev/null +++ b/Iris Dataset/KNN-IrisData.ipynb @@ -0,0 +1,629 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Classification \n", + "predict which group a new target object belongs to by comparing it to identified objects. The identified, or labeled objects are called the training set." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## KNN - K-Nearest Neighbors\n", + "Find the k nearest objects to the target object using some distance metric. Then these k nearest neighbors get to vote on the identity of the target object. \n", + "For example, if k=5, we find the 5 nearest objects in our training set. If three of them are apples, one is a pear and one is an orange then we predict our target object is an apple. " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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idsepal_lengthsepal_widthpetal_lengthpetal_widthspecies
005.13.51.40.2Iris-setosa
114.93.01.40.2Iris-setosa
224.73.21.30.2Iris-setosa
334.63.11.50.2Iris-setosa
445.03.61.40.2Iris-setosa
\n", + "
" + ], + "text/plain": [ + " id sepal_length sepal_width petal_length petal_width species\n", + "0 0 5.1 3.5 1.4 0.2 Iris-setosa\n", + "1 1 4.9 3.0 1.4 0.2 Iris-setosa\n", + "2 2 4.7 3.2 1.3 0.2 Iris-setosa\n", + "3 3 4.6 3.1 1.5 0.2 Iris-setosa\n", + "4 4 5.0 3.6 1.4 0.2 Iris-setosa" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "train = pd.read_csv('iris.data')\n", + "train.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Rename columns of training set, and add a column for distance." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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0123speciesdistance
05.13.51.40.2Iris-setosa9999
14.93.01.40.2Iris-setosa9999
24.73.21.30.2Iris-setosa9999
34.63.11.50.2Iris-setosa9999
45.03.61.40.2Iris-setosa9999
\n", + "
" + ], + "text/plain": [ + " 0 1 2 3 species distance\n", + "0 5.1 3.5 1.4 0.2 Iris-setosa 9999\n", + "1 4.9 3.0 1.4 0.2 Iris-setosa 9999\n", + "2 4.7 3.2 1.3 0.2 Iris-setosa 9999\n", + "3 4.6 3.1 1.5 0.2 Iris-setosa 9999\n", + "4 5.0 3.6 1.4 0.2 Iris-setosa 9999" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "train = train.drop('id', 1)\n", + "cols = ['sepal_length', 'sepal_width', 'petal_length', 'petal_width']\n", + "train.rename(columns = {cols[0]:0, cols[1]:1, cols[2]:2, cols[3]:3}, inplace=True)\n", + "train['distance'] = 9999\n", + "train.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create an unidentified Target instance, then we will try to predict its species using knn." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 7.0\n", + "1 3.1\n", + "2 5.6\n", + "3 1.9\n", + "dtype: float64" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "target = pd.Series([7.0, 3.1, 5.6, 1.9])\n", + "target" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Distance\n", + "There are a variety of ways to measure [distance](https://towardsdatascience.com/9-distance-measures-in-data-science-918109d069fa). If there are many attributes, we may use a subset of the attributes to compare objects. \n", + "We'll use Euclidean distance, similar to Pythagorean Theorem but scaled to more attributes. \n", + "We compute the distance of every training instance from the target." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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0123speciesdistance
05.13.51.40.2Iris-setosa4.929503
105.43.71.50.2Iris-setosa4.756049
205.43.41.70.2Iris-setosa4.555217
304.83.11.60.2Iris-setosa4.871345
405.03.51.30.3Iris-setosa5.020956
507.03.24.71.4Iris-versicolor1.034408
605.02.03.51.0Iris-versicolor3.229551
705.93.24.81.8Iris-versicolor1.367479
805.52.43.81.1Iris-versicolor2.572936
905.52.64.41.2Iris-versicolor2.104757
1006.33.36.02.5Iris-virginica1.024695
1106.53.25.12.0Iris-virginica0.721110
1206.93.25.72.3Iris-virginica0.435890
1307.42.86.11.9Iris-virginica0.707107
1406.73.15.62.4Iris-virginica0.583095
\n", + "
" + ], + "text/plain": [ + " 0 1 2 3 species distance\n", + "0 5.1 3.5 1.4 0.2 Iris-setosa 4.929503\n", + "10 5.4 3.7 1.5 0.2 Iris-setosa 4.756049\n", + "20 5.4 3.4 1.7 0.2 Iris-setosa 4.555217\n", + "30 4.8 3.1 1.6 0.2 Iris-setosa 4.871345\n", + "40 5.0 3.5 1.3 0.3 Iris-setosa 5.020956\n", + "50 7.0 3.2 4.7 1.4 Iris-versicolor 1.034408\n", + "60 5.0 2.0 3.5 1.0 Iris-versicolor 3.229551\n", + "70 5.9 3.2 4.8 1.8 Iris-versicolor 1.367479\n", + "80 5.5 2.4 3.8 1.1 Iris-versicolor 2.572936\n", + "90 5.5 2.6 4.4 1.2 Iris-versicolor 2.104757\n", + "100 6.3 3.3 6.0 2.5 Iris-virginica 1.024695\n", + "110 6.5 3.2 5.1 2.0 Iris-virginica 0.721110\n", + "120 6.9 3.2 5.7 2.3 Iris-virginica 0.435890\n", + "130 7.4 2.8 6.1 1.9 Iris-virginica 0.707107\n", + "140 6.7 3.1 5.6 2.4 Iris-virginica 0.583095" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "train['distance'] = ((train.loc[:,0]-target[0])**2 + (train.loc[:,1]-target[1])**2 + (train.loc[:,2]-target[2])**2 + (train.loc[:,3]-target[3])**2) ** 0.5\n", + "train.loc[::10]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We sort the training records by distance, and add the species of the (k=7) items nearest to the target to a list." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['Iris-virginica',\n", + " 'Iris-virginica',\n", + " 'Iris-virginica',\n", + " 'Iris-virginica',\n", + " 'Iris-virginica',\n", + " 'Iris-virginica',\n", + " 'Iris-virginica']" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "k = 7\n", + "train = train.sort_values('distance', ascending=True)\n", + "knn = list(train.head(k).species)\n", + "knn" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We use mode to get the most popular of the knn list. In this example the whole knn list is Iris-virginica, so our prediction is obvious. But sometimes the list of nearest neighbors will be a variety, and the mode tells us our prediction." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Iris-virginica\n" + ] + } + ], + "source": [ + "from statistics import mode\n", + "print(mode(knn))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To check our prediction, we plot the training set on a scatter plot, then plot our target. Here we can see our target is surrounded by Iris-virginica instances, so our prediction is probably correct." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Iris Data Scatter Plot')" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "colors = {'Iris-setosa':'red', 'Iris-virginica':'blue', 'Iris-versicolor':'green'}\n", + "plt.scatter(\n", + " train[2], \n", + " train[3], \n", + " c=train['species'].map(colors))\n", + "plt.scatter(target[2], target[3], c='orange')\n", + "plt.xlabel(cols[2])\n", + "plt.ylabel(cols[3])\n", + "plt.title('Iris Data Scatter Plot')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "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": "3.7.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 87f407702a4a8ba18591daeb10fb8a1e5fced93d Mon Sep 17 00:00:00 2001 From: Joe James Date: Mon, 1 May 2023 12:35:08 -0700 Subject: [PATCH 50/53] Add files via upload --- flatten_list.py | 27 +++++++++++++++++++++++++++ 1 file changed, 27 insertions(+) create mode 100644 flatten_list.py diff --git a/flatten_list.py b/flatten_list.py new file mode 100644 index 00000000..3f3c57df --- /dev/null +++ b/flatten_list.py @@ -0,0 +1,27 @@ +# Python Flatten Nested Lists +# (c) Joe James 2023 + +# list comprehension method +def flatten1 (myList): + return [i for j in myList for i in j] + +# recursive method +def flatten2 (myList): + flatList = [] + for item in myList: + if isinstance(item, list): + flatList.extend(flatten2(item)) + else: + flatList.append(item) + return flatList + +list1 = [[0], [1, 2], [3, [4, 5]], [6], [7]] +list2 = [0, [1, 2], [3, [4, 5]], [6], 7] + +print("flatten1(list1): ", flatten1(list1)) # works, but only flattens 1 layer of sublists +# print(flatten1(list2)) # error - can't work with list of ints and sublists of ints + +print("flatten2(list1): ", flatten2(list1)) +print("flatten2(list2): ", flatten2(list2)) + + From 57dd4054fa11b8188cb414453e12541fd7b0adf8 Mon Sep 17 00:00:00 2001 From: Joe James Date: Tue, 9 May 2023 11:04:27 -0700 Subject: [PATCH 51/53] Add files via upload --- dict_comprehensions.py | 54 ++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 54 insertions(+) create mode 100644 dict_comprehensions.py diff --git a/dict_comprehensions.py b/dict_comprehensions.py new file mode 100644 index 00000000..890c7221 --- /dev/null +++ b/dict_comprehensions.py @@ -0,0 +1,54 @@ +# Python Dictionary Comprehensions +# (c) Joe James 2023 + +# 1. math function to compute values using list +dict1 = {x: 2*x for x in [0, 2, 4, 6]} +print ('1. ', dict1) + +# 2. math function to compute values using range +dict2 = {x: x**2 for x in range(0, 7, 2)} +print ('2. ', dict2) + +# 3. from chars in a string +dict3 = {x: ord(x) for x in 'Kumar'} +print ('3. ', dict3) + +# 4. given lists of keys & values +x = ['Aditii', 'Brandon', 'Clumley', 'Magomed', 'Rishi'] +y = [1, 2, 3, 13, 18] +dict4 = {i: j for (i,j) in zip(x,y)} +print ('4. ', dict4) + +# 5. from chars in a string +x = "python" +dict5 = {i: 3*i.upper() for i in x} +print('5. ', dict5) + +# 6. list comprehension for the value +x = [2, 4, 6, 8] +y = [5, 10, 15, 20] +dict6 = {i: [i + 2*j for j in y] for i in x} +print('6. ', dict6) + +#7. using items +x = {'A':10, 'B':20, 'C':30} +dict7 = {i: j*2 for (i,j) in x.items()} +print('7. ', dict7) + +# 8. conditional comprehension +dict8 = {i: i**3 for i in range(10) if i%2 == 0} +print('8. ', dict8) + +# 9. if-else conditional comprehension +x = {'A':10, 'B':20, 'C':30} +dict9 = {i: (j if j < 15 else j+100) for (i,j) in x.items()} +print('9. ', dict9) + +# 10. transformation from an existing dict +x = {'A':10, 'B':20, 'C':30} +dict10 = {i: x[i]+1 for i in x} +print('10. ', dict10) + + + + From 4f3e3a8180e9f9f0ce41ef12ea6d716510c4d7c7 Mon Sep 17 00:00:00 2001 From: Joe James Date: Thu, 11 May 2023 12:11:57 -0700 Subject: [PATCH 52/53] Add files via upload --- remove_from_list.py | 48 +++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 48 insertions(+) create mode 100644 remove_from_list.py diff --git a/remove_from_list.py b/remove_from_list.py new file mode 100644 index 00000000..9619664f --- /dev/null +++ b/remove_from_list.py @@ -0,0 +1,48 @@ +# Python: del vs pop vs remove from a list +# (c) Joe James 2023 + +def get_dogs(): + return ['Fido', 'Rover', 'Spot', 'Duke', 'Chip', 'Spot'] + +dogs = get_dogs() +print(dogs) + +# Use pop() to remove last item or an item by index and get the returned value. +print('1. pop last item from list:') +myDog = dogs.pop() +print(myDog, dogs) + +dogs = get_dogs() +print('2. pop item with index 1:') +myDog = dogs.pop(1) +print(myDog, dogs) + +# Use remove() to delete an item by value. (raises ValueError if value not found) +dogs = get_dogs() +print('3. remove first Spot from list:') +dogs.remove('Spot') +print(dogs) + +# Use del to remove an item or range of items by index. Or delete entire list. +dogs = get_dogs() +print('4. del item with index 3:') +del(dogs[3]) +print(dogs) + +dogs = get_dogs() +print('5. del items [1:3] from list:') +del(dogs[1:3]) +print(dogs) + +dogs = get_dogs() +print('6. del entire list:') +del(dogs) +print(dogs) + + + + + + + + From fa2904f71169871485b5fec8f0be0441a8b23f1a Mon Sep 17 00:00:00 2001 From: Joe James Date: Mon, 15 May 2023 11:07:11 -0700 Subject: [PATCH 53/53] Add files via upload --- deep_copy.ipynb | 248 ++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 248 insertions(+) create mode 100644 deep_copy.ipynb diff --git a/deep_copy.ipynb b/deep_copy.ipynb new file mode 100644 index 00000000..a11d7052 --- /dev/null +++ b/deep_copy.ipynb @@ -0,0 +1,248 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Python: how to Copy and Deep Copy Python Lists \n", + "(c) Joe James 2023" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Assignment is not a Copy\n", + "listA = listB does not create a copy. Changes to one list will be reflected in the other.\n", + "listA and listB both reference the exact same list." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2, 44, 6, [1, 3]]\n", + "140554034568968\n", + "140554034568968\n" + ] + } + ], + "source": [ + "listA = [2, 4, 6, [1, 3]]\n", + "listB = listA\n", + "listB[1] = 44\n", + "print(listA)\n", + "print(id(listA))\n", + "print(id(listB))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Shallow copy using the list() constructor\n", + "Shallow copy only works for 1D lists of native data types. \n", + "Sublists, dicts, and other objects will retain the same referece to those objects." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2, 4, 6, [55, 3]]\n" + ] + } + ], + "source": [ + "listA = [2, 4, 6, [1, 3]]\n", + "listB = list(listA)\n", + "listB[1] = 44\n", + "listB[3][0] = 55\n", + "print(listA)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Other ways to make a Shallow copy\n", + "List comprehensions, list.copy(), or copy.copy() can also be used to make *shallow* copies" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2, 4, 6, [55, 3]]\n" + ] + } + ], + "source": [ + "listA = [2, 4, 6, [1, 3]]\n", + "listB = [x for x in listA]\n", + "listB[1] = 44\n", + "listB[3][0] = 55\n", + "print(listA)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2, 4, 6, [55, 3]]\n" + ] + } + ], + "source": [ + "listA = [2, 4, 6, [1, 3]]\n", + "listB = listA.copy()\n", + "listB[1] = 44\n", + "listB[3][0] = 55\n", + "print(listA)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2, 4, 6, [55, 3]]\n" + ] + } + ], + "source": [ + "import copy\n", + "listA = [2, 4, 6, [1, 3]]\n", + "listB = copy.copy(listA)\n", + "listB[1] = 44\n", + "listB[3][0] = 55\n", + "print(listA)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### How to Deep Copy a Python List\n", + "use copy.deepcopy()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2, 4, 6, [1, 3]]\n" + ] + } + ], + "source": [ + "import copy\n", + "listA = [2, 4, 6, [1, 3]]\n", + "listB = copy.deepcopy(listA)\n", + "listB[1] = 44\n", + "listB[3][0] = 55\n", + "print(listA)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Deepcopy with Objects" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "140554035637216 140554035637104\n", + "140554035637216 140554035637216\n", + "140554035637216 140554035637048\n" + ] + } + ], + "source": [ + "class Pony():\n", + " def __init__(self, n):\n", + " self.name = n\n", + " \n", + "# copy.copy on an object gives you 2 unique objects (with same attributes)\n", + "pony1 = Pony('Pinto')\n", + "pony2 = copy.copy(pony1)\n", + "print(id(pony1), id(pony2))\n", + "\n", + "# copy.copy on a list of objects gives you 2 unique lists containing the exact same objects \n", + "# (ie. new list is a shallow copy)\n", + "m = [pony1, pony2]\n", + "n = copy.copy (m)\n", + "print(id(m[0]), id(n[0]))\n", + "\n", + "# use copy.deepcopy to deep copy a list of objects\n", + "n = copy.deepcopy (m)\n", + "print(id(m[0]), id(n[0]))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "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": "3.7.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}