diff --git a/.DS_Store b/.DS_Store new file mode 100644 index 0000000..4e189d7 Binary files /dev/null and b/.DS_Store differ diff --git a/your-code/.DS_Store b/your-code/.DS_Store new file mode 100644 index 0000000..62f9575 Binary files /dev/null and b/your-code/.DS_Store differ diff --git a/your-code/.ipynb_checkpoints/challenge-1-checkpoint.ipynb b/your-code/.ipynb_checkpoints/challenge-1-checkpoint.ipynb new file mode 100644 index 0000000..1833beb --- /dev/null +++ b/your-code/.ipynb_checkpoints/challenge-1-checkpoint.ipynb @@ -0,0 +1,359 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Challenge 1: Tuples\n", + "\n", + "#### Do you know you can create tuples with only one element?\n", + "\n", + "**In the cell below, define a variable `tup` with a single element `\"I\"`.**\n", + "\n", + "*Hint: you need to add a comma (`,`) after the single element.*" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# Your code here\n", + "tup = (\"I\",)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Print the type of `tup`. \n", + "\n", + "Make sure its type is correct (i.e. *tuple* instead of *str*)." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "# Your code here\n", + "print(type(tup))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Now try to append the following elements to `tup`. \n", + "\n", + "Are you able to do it? Explain.\n", + "\n", + "```\n", + "\"r\", \"o\", \"n\", \"h\", \"a\", \"c\", \"k',\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "ename": "AttributeError", + "evalue": "'tuple' object has no attribute 'append'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "Input \u001b[0;32mIn [3]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# Your code here\u001b[39;00m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;66;03m# list_example=[\"I\",]\u001b[39;00m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;66;03m# list_example.extend([\"r\",\"o\",\"n\",\"h\",\"a\",\"c\",\"k\"])\u001b[39;00m\n\u001b[0;32m----> 4\u001b[0m \u001b[43mtup\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mappend\u001b[49m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 5\u001b[0m tup\u001b[38;5;241m.\u001b[39mextend(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124m\"\u001b[39m,\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mo\u001b[39m\u001b[38;5;124m\"\u001b[39m,\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mn\u001b[39m\u001b[38;5;124m\"\u001b[39m,\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mh\u001b[39m\u001b[38;5;124m\"\u001b[39m,\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124ma\u001b[39m\u001b[38;5;124m\"\u001b[39m,\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mc\u001b[39m\u001b[38;5;124m\"\u001b[39m,\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mk\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", + "\u001b[0;31mAttributeError\u001b[0m: 'tuple' object has no attribute 'append'" + ] + } + ], + "source": [ + "# Your code here\n", + "# list_example=[\"I\",]\n", + "# list_example.extend([\"r\",\"o\",\"n\",\"h\",\"a\",\"c\",\"k\"])\n", + "tup.append(\"r\")\n", + "tup.extend(\"r\",\"o\",\"n\",\"h\",\"a\",\"c\",\"k\")\n", + "# Your explanation here\n", + "# We cannot mutate tuples due to these data structures are immutable, which means the value inside of a tuple cannot be overwritten." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### How about re-assign a new value to an existing tuple?\n", + "\n", + "Re-assign the following elements to `tup`. Are you able to do it? Explain.\n", + "\n", + "```\n", + "\"I\", \"r\", \"o\", \"n\", \"h\", \"a\", \"c\", \"k\"\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "('I',)\n", + "4314244384\n", + "4368773184\n", + "('I', 'r', 'o', 'n', 'h', 'a', 'c', 'k')\n" + ] + } + ], + "source": [ + "# Your code here\n", + "# We can reassign a tuple \n", + "print(tup)\n", + "print(id(tup))\n", + "tup=(\"I\", \"r\", \"o\", \"n\", \"h\", \"a\", \"c\", \"k\")\n", + "print(id(tup))\n", + "\n", + "# We can convert a tuple to List to mutate it and the convert it to a tuple:\n", + "\n", + "# Your explanation here\n", + "print(tup)\n", + "# In the first example we are not mutating the tuple itself because the tuple is pointing to another space on memory, not the same value. We can prove it checking the ids of the variables before an after the reassignment " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Split `tup` into `tup1` and `tup2` with 4 elements in each. \n", + "\n", + "`tup1` should be `(\"I\", \"r\", \"o\", \"n\")` and `tup2` should be `(\"h\", \"a\", \"c\", \"k\")`.\n", + "\n", + "*Hint: use positive index numbers for `tup1` assignment and use negative index numbers for `tup2` assignment. Positive index numbers count from the beginning whereas negative index numbers count from the end of the sequence.*\n", + "\n", + "Also print `tup1` and `tup2`." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "('I', 'r', 'o', 'n')\n", + "('h', 'a', 'c', 'k')\n" + ] + } + ], + "source": [ + "# Your code here\n", + "tup1 = tup[:4]\n", + "tup2 = tup[-4:]\n", + "print(tup1)\n", + "print(tup2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Add `tup1` and `tup2` into `tup3` using the `+` operator.\n", + "\n", + "Then print `tup3` and check if `tup3` equals to `tup`." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "('I', 'r', 'o', 'n', 'h', 'a', 'c', 'k')\n", + "True\n" + ] + } + ], + "source": [ + "# Your code here\n", + "tup3 = tup1 + tup2\n", + "print(tup3)\n", + "print(tup3==tup)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Count the number of elements in `tup1` and `tup2`. Then add the two counts together and check if the sum is the same as the number of elements in `tup3`" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "True\n" + ] + } + ], + "source": [ + "# Your code here\n", + "print(len(tup1)+len(tup2) == len(tup3))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### What is the index number of `\"h\"` in `tup3`?" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The index of \"h\" in tup3 is 4\n" + ] + } + ], + "source": [ + "# Your code here\n", + "print(f'The index of \"h\" in tup3 is {tup3.index(\"h\")}')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Now, use a FOR loop to check whether each letter in the following list is present in `tup3`:\n", + "\n", + "```\n", + "letters = [\"a\", \"b\", \"c\", \"d\", \"e\"]\n", + "```\n", + "\n", + "For each letter you check, print `True` if it is present in `tup3` otherwise print `False`.\n", + "\n", + "*Hint: you only need to loop `letters`. You don't need to loop `tup3` because there is a Python operator `in` you can use. See [reference](https://stackoverflow.com/questions/17920147/how-to-check-if-a-tuple-contains-an-element-in-python).*" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[True, False, True, False, False]\n", + "[True, False, True, False, False]\n", + "[True, False, True, False, False]\n" + ] + } + ], + "source": [ + "# Your code here\n", + "letters = [\"a\", \"b\", \"c\", \"d\", \"e\"]\n", + "\n", + "output_letters = [True if letter in tup3 else False for letter in letters]\n", + "\n", + "output=list(map(lambda letter: True if letter in tup3 else False, letters))\n", + "\n", + "output_1=[True if ele in tup3 else False for ele in letters]\n", + "\n", + "print(output_letters)\n", + "print(output)\n", + "print(output_1)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### How many times does each letter in `letters` appear in `tup3`?\n", + "\n", + "Print out the number of occurrence of each letter." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'a': 1, 'b': 0, 'c': 1, 'd': 0, 'e': 0}\n" + ] + } + ], + "source": [ + "# Your code here\n", + "\n", + "# [\"a\",\"b\", \"c\", \"a\", \"c\"]\n", + "count_letters = {letter: tup3.count(letter) for letter in letters}\n", + "\n", + "print(count_letters)\n", + "\n", + " " + ] + }, + { + "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": "3.8.9" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/your-code/.ipynb_checkpoints/challenge-2-checkpoint.ipynb b/your-code/.ipynb_checkpoints/challenge-2-checkpoint.ipynb new file mode 100644 index 0000000..0062327 --- /dev/null +++ b/your-code/.ipynb_checkpoints/challenge-2-checkpoint.ipynb @@ -0,0 +1,522 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Challenge 2: Sets\n", + "\n", + "There are a lot to learn about Python Sets and the information presented in the lesson is limited due to its length. To learn Python Sets in depth you are strongly encouraged to review the W3Schools tutorial on [Python Sets Examples and Methods](https://www.w3schools.com/python/python_sets.asp) before you work on this lab. Some difficult questions in this lab have their solutions in the W3Schools tutorial.\n", + "\n", + "#### First, import the Python `random` libary" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import random" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### In the cell below, create a list named `sample_list_1` with 80 random values. \n", + "\n", + "Requirements:\n", + "\n", + "* Each value is an integer falling between 0 and 100.\n", + "* Each value in the list is unique.\n", + "\n", + "Print `sample_list_1` to review its values\n", + "\n", + "*Hint: use `random.sample` ([reference](https://docs.python.org/3/library/random.html#random.sample)).*" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[12, 7, 31, 84, 47, 62, 5, 65, 4, 36, 40, 56, 2, 75, 97, 16, 27, 50, 44, 95, 9, 66, 45, 39, 98, 11, 61, 96, 42, 22, 85, 72, 76, 43, 99, 64, 17, 26, 37, 13, 74, 82, 83, 58, 0, 49, 88, 10, 51, 33, 15, 78, 34, 19, 86, 8, 57, 67, 30, 55, 69, 87, 23, 18, 79, 35, 24, 46, 63, 21, 60, 1, 20, 91, 53, 100, 73, 93, 25, 90]\n" + ] + }, + { + "data": { + "text/plain": [ + "80" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Your code here\n", + "sample_list_1 = random.sample(range(0,101), 80)\n", + "print(sample_list_1)\n", + "len(sample_list_1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Convert `sample_list_1` to a set called `set1`. Print the length of the set. Is its length still 80?" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "80" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Your code here\n", + "set1 =set(sample_list_1)\n", + "len(set1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Create another list named `sample_list_2` with 80 random values.\n", + "\n", + "Requirements:\n", + "\n", + "* Each value is an integer falling between 0 and 100.\n", + "* The values in the list don't have to be unique.\n", + "\n", + "*Hint: Use a FOR loop.*" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[97, 14, 28, 64, 60, 68, 60, 15, 25, 8, 76, 10, 31, 82, 11, 66, 31, 82, 31, 62, 73, 5, 83, 52, 41, 45, 0, 35, 2, 72, 79, 4, 44, 20, 46, 96, 41, 100, 49, 64, 46, 31, 70, 46, 95, 40, 37, 13, 96, 88, 85, 83, 30, 86, 0, 77, 82, 39, 36, 27, 31, 69, 91, 90, 96, 61, 71, 44, 40, 16, 0, 40, 89, 77, 84, 87, 73, 5, 29, 91]\n", + "80\n", + "[7, 39, 79, 83, 91, 78, 66, 7, 53, 3, 86, 90, 2, 38, 97, 70, 100, 18, 70, 25, 13, 84, 25, 73, 17, 9, 54, 37, 79, 95, 47, 14, 23, 36, 98, 29, 9, 26, 20, 70, 87, 99, 37, 99, 16, 76, 85, 75, 38, 32, 44, 14, 96, 53, 77, 90, 34, 62, 92, 73, 32, 68, 56, 51, 64, 24, 18, 57, 16, 1, 54, 25, 10, 88, 61, 25, 71, 71, 26, 65]\n", + "80\n" + ] + } + ], + "source": [ + "# Your code here\n", + "sample_list_2 =[]\n", + "for _ in range(80):\n", + " # randint takes from 0 to 100\n", + " sample_list_2.append(random.randint(0,100))\n", + "\n", + "print(sample_list_2)\n", + "print(len(sample_list_2))\n", + "sample_list2_copy = [random.randint(0,100) for _ in range(80)]\n", + "print(sample_list2_copy)\n", + "print(len(sample_list2_copy))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Convert `sample_list_2` to a set called `set2`. Print the length of the set. Is its length still 80?" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "57" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Your code here\n", + "\n", + "set2 = set(sample_list_2)\n", + "len(set2)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Identify the elements present in `set1` but not in `set2`. Assign the elements to a new set named `set3`." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{1, 3, 9, 17, 18, 19, 21, 23, 24, 26, 32, 33, 38, 42, 43, 47, 48, 50, 51, 53, 54, 55, 57, 59, 63, 65, 74, 75, 78, 80, 81, 92, 94, 98, 99}\n" + ] + } + ], + "source": [ + "# Your code here\n", + "set3= set1.difference(set2)\n", + "print(set3)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Identify the elements present in `set2` but not in `set1`. Assign the elements to a new set named `set4`." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{2, 70, 39, 73, 76, 13, 79, 52, 85, 86, 88, 91}\n" + ] + } + ], + "source": [ + "# Your code here\n", + "set4 = set2.difference(set1)\n", + "print(set4)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Now Identify the elements shared between `set1` and `set2`. Assign the elements to a new set named `set5`." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{0, 4, 5, 8, 10, 11, 14, 15, 16, 20, 25, 27, 28, 29, 30, 31, 35, 36, 37, 40, 41, 44, 45, 46, 49, 60, 61, 62, 64, 66, 68, 69, 71, 72, 77, 82, 83, 84, 87, 89, 90, 95, 96, 97, 100}\n" + ] + } + ], + "source": [ + "# Your code here\n", + "set5 = set1.intersection(set2)\n", + "print(set5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### What is the relationship among the following values:\n", + "\n", + "* len(set1)\n", + "* len(set2)\n", + "* len(set3)\n", + "* len(set4)\n", + "* len(set5)\n", + "\n", + "Use a math formular to represent that relationship. Test your formular with Python code." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "80\n", + "57\n", + "35\n", + "12\n", + "45\n", + "True\n", + "True\n", + "False\n" + ] + } + ], + "source": [ + "# Your code here\n", + "print(len(set1))\n", + "print(len(set2))\n", + "print(len(set3))\n", + "print(len(set4))\n", + "print(len(set5))\n", + "\n", + "print(set1==set3.union(set5))\n", + "print(set4==set2.difference(set5))\n", + "print(set5==set4.union(set3))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Create an empty set called `set6`." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "set()\n" + ] + } + ], + "source": [ + "# Your code here\n", + "set6=set()\n", + "print(set6)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Add `set3` and `set5` to `set6` using the Python Set `update` method." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{0, 1, 3, 4, 5, 8, 9, 10, 11, 14, 15, 16, 17, 18, 19, 20, 21, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 35, 36, 37, 38, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 53, 54, 55, 57, 59, 60, 61, 62, 63, 64, 65, 66, 68, 69, 71, 72, 74, 75, 77, 78, 80, 81, 82, 83, 84, 87, 89, 90, 92, 94, 95, 96, 97, 98, 99, 100}\n" + ] + } + ], + "source": [ + "# Your code here\n", + "set6.update(set3,set5)\n", + "print(set6)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Check if `set1` and `set6` are equal." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "True\n" + ] + } + ], + "source": [ + "# Your code here\n", + "print(set1 ==set6)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Check if `set1` contains `set2` using the Python Set `issubset` method. Then check if `set1` contains `set3`.*" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "False\n", + "True\n" + ] + } + ], + "source": [ + "# Your code here\n", + "print(set2.issubset(set1))\n", + "print(set3.issubset(set1))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Using the Python Set `union` method, aggregate `set3`, `set4`, and `set5`. Then aggregate `set1` and `set2`. \n", + "\n", + "#### Check if the aggregated values are equal." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "True\n" + ] + } + ], + "source": [ + "# Your code here\n", + "aggregate_1=set3.union(set4, set5)\n", + "aggregate_2=set1.union(set2)\n", + "print(aggregate_1 == aggregate_2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Using the `pop` method, remove the first element from `set1`." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "79" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Your code here\n", + "set1= list (set1)\n", + "removed = set1.pop(0)\n", + "set1 = set(set1)\n", + "len(set1)\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Remove every element in the following list from `set1` if they are present in the set. Print the remaining elements.\n", + "\n", + "```\n", + "list_to_remove = [1, 9, 11, 19, 21, 29, 31, 39, 41, 49, 51, 59, 61, 69, 71, 79, 81, 89, 91, 99]\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[3, 4, 5, 8, 10, 14, 15, 16, 17, 18, 20, 23, 24, 25, 26, 27, 28, 30, 32, 33, 35, 36, 37, 38, 40, 42, 43, 44, 45, 46, 47, 48, 50, 53, 54, 55, 57, 60, 62, 63, 64, 65, 66, 68, 72, 74, 75, 77, 78, 80, 82, 83, 84, 87, 90, 92, 94, 95, 96, 97, 98, 100]\n" + ] + } + ], + "source": [ + "# Your code here\n", + "\n", + "list_to_remove = [1, 9, 11, 19, 21, 29, 31, 39, 41, 49, 51, 59, 61, 69, 71, 79, 81, 89, 91, 99]\n", + "set1 = [ele for ele in set1 if ele not in list_to_remove ]\n", + "print(set1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "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": "3.8.9" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/your-code/.ipynb_checkpoints/challenge-3-checkpoint.ipynb b/your-code/.ipynb_checkpoints/challenge-3-checkpoint.ipynb new file mode 100644 index 0000000..a00a79b --- /dev/null +++ b/your-code/.ipynb_checkpoints/challenge-3-checkpoint.ipynb @@ -0,0 +1,259 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Challenge 3: Dictionaries\n", + "\n", + "In this challenge you will practice how to manipulate Python dictionaries. Before starting on this challenge, you are encouraged to review W3School's [Python Dictionary Examples and Methods](https://www.w3schools.com/python/python_dictionaries.asp).\n", + "\n", + "First thing you will practice is how to sort the keys in a dictionary. Unlike the list object, Python dictionary does not have a built-in *sort* method. You'll need to use FOR loops to to sort dictionaries either by key or by value.\n", + "\n", + "The dictionary below is a summary of the word frequency of Ed Sheeran's song *Shape of You*. Each key is a word in the lyrics and the value is the number of times that word appears in the lyrics." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "word_freq = {'love': 25, 'conversation': 1, 'every': 6, \"we're\": 1, 'plate': 1, 'sour': 1, 'jukebox': 1, 'now': 11, 'taxi': 1, 'fast': 1, 'bag': 1, 'man': 1, 'push': 3, 'baby': 14, 'going': 1, 'you': 16, \"don't\": 2, 'one': 1, 'mind': 2, 'backseat': 1, 'friends': 1, 'then': 3, 'know': 2, 'take': 1, 'play': 1, 'okay': 1, 'so': 2, 'begin': 1, 'start': 2, 'over': 1, 'body': 17, 'boy': 2, 'just': 1, 'we': 7, 'are': 1, 'girl': 2, 'tell': 1, 'singing': 2, 'drinking': 1, 'put': 3, 'our': 1, 'where': 1, \"i'll\": 1, 'all': 1, \"isn't\": 1, 'make': 1, 'lover': 1, 'get': 1, 'radio': 1, 'give': 1, \"i'm\": 23, 'like': 10, 'can': 1, 'doing': 2, 'with': 22, 'club': 1, 'come': 37, 'it': 1, 'somebody': 2, 'handmade': 2, 'out': 1, 'new': 6, 'room': 3, 'chance': 1, 'follow': 6, 'in': 27, 'may': 2, 'brand': 6, 'that': 2, 'magnet': 3, 'up': 3, 'first': 1, 'and': 23, 'pull': 3, 'of': 6, 'table': 1, 'much': 2, 'last': 3, 'i': 6, 'thrifty': 1, 'grab': 2, 'was': 2, 'driver': 1, 'slow': 1, 'dance': 1, 'the': 18, 'say': 2, 'trust': 1, 'family': 1, 'week': 1, 'date': 1, 'me': 10, 'do': 3, 'waist': 2, 'smell': 3, 'day': 6, 'although': 3, 'your': 21, 'leave': 1, 'want': 2, \"let's\": 2, 'lead': 6, 'at': 1, 'hand': 1, 'how': 1, 'talk': 4, 'not': 2, 'eat': 1, 'falling': 3, 'about': 1, 'story': 1, 'sweet': 1, 'best': 1, 'crazy': 2, 'let': 1, 'too': 5, 'van': 1, 'shots': 1, 'go': 2, 'to': 2, 'a': 8, 'my': 33, 'is': 5, 'place': 1, 'find': 1, 'shape': 6, 'on': 40, 'kiss': 1, 'were': 3, 'night': 3, 'heart': 3, 'for': 3, 'discovering': 6, 'something': 6, 'be': 16, 'bedsheets': 3, 'fill': 2, 'hours': 2, 'stop': 1, 'bar': 1}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Sort the keys of `word_freq` ascendingly.\n", + "\n", + "Please create a new dictionary called `word_freq2` based on `word_freq` with the keys sorted ascedingly.\n", + "\n", + "There are several ways to achieve that goal but many of the ways are beyond what we have covered so far in the course. There is one way that we'll describe employing what you have learned. Please feel free to use this way or any other way you want.\n", + "\n", + "1. First extract the keys of `word_freq` and convert it to a list called `keys`.\n", + "\n", + "1. Sort the `keys` list.\n", + "\n", + "1. Create an empty dictionary `word_freq2`.\n", + "\n", + "1. Use a FOR loop to iterate each value in `keys`. For each key iterated, find the corresponding value in `word_freq` and insert the key-value pair to `word_freq2`.\n", + "\n", + "Print out `word_freq2` to examine its keys and values. Your output should be:\n", + "\n", + "```python\n", + "{'a': 8, 'about': 1, 'all': 1, 'although': 3, 'and': 23, 'are': 1, 'at': 1, 'baby': 14, 'backseat': 1, 'bag': 1, 'bar': 1, 'be': 16, 'bedsheets': 3, 'begin': 1, 'best': 1, 'body': 17, 'boy': 2, 'brand': 6, 'can': 1, 'chance': 1, 'club': 1, 'come': 37, 'conversation': 1, 'crazy': 2, 'dance': 1, 'date': 1, 'day': 6, 'discovering': 6, 'do': 3, 'doing': 2, \"don't\": 2, 'drinking': 1, 'driver': 1, 'eat': 1, 'every': 6, 'falling': 3, 'family': 1, 'fast': 1, 'fill': 2, 'find': 1, 'first': 1, 'follow': 6, 'for': 3, 'friends': 1, 'get': 1, 'girl': 2, 'give': 1, 'go': 2, 'going': 1, 'grab': 2, 'hand': 1, 'handmade': 2, 'heart': 3, 'hours': 2, 'how': 1, 'i': 6, \"i'll\": 1, \"i'm\": 23, 'in': 27, 'is': 5, \"isn't\": 1, 'it': 1, 'jukebox': 1, 'just': 1, 'kiss': 1, 'know': 2, 'last': 3, 'lead': 6, 'leave': 1, 'let': 1, \"let's\": 2, 'like': 10, 'love': 25, 'lover': 1, 'magnet': 3, 'make': 1, 'man': 1, 'may': 2, 'me': 10, 'mind': 2, 'much': 2, 'my': 33, 'new': 6, 'night': 3, 'not': 2, 'now': 11, 'of': 6, 'okay': 1, 'on': 40, 'one': 1, 'our': 1, 'out': 1, 'over': 1, 'place': 1, 'plate': 1, 'play': 1, 'pull': 3, 'push': 3, 'put': 3, 'radio': 1, 'room': 3, 'say': 2, 'shape': 6, 'shots': 1, 'singing': 2, 'slow': 1, 'smell': 3, 'so': 2, 'somebody': 2, 'something': 6, 'sour': 1, 'start': 2, 'stop': 1, 'story': 1, 'sweet': 1, 'table': 1, 'take': 1, 'talk': 4, 'taxi': 1, 'tell': 1, 'that': 2, 'the': 18, 'then': 3, 'thrifty': 1, 'to': 2, 'too': 5, 'trust': 1, 'up': 3, 'van': 1, 'waist': 2, 'want': 2, 'was': 2, 'we': 7, \"we're\": 1, 'week': 1, 'were': 3, 'where': 1, 'with': 22, 'you': 16, 'your': 21}\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'a': 8, 'about': 1, 'all': 1, 'although': 3, 'and': 23, 'are': 1, 'at': 1, 'baby': 14, 'backseat': 1, 'bag': 1, 'bar': 1, 'be': 16, 'bedsheets': 3, 'begin': 1, 'best': 1, 'body': 17, 'boy': 2, 'brand': 6, 'can': 1, 'chance': 1, 'club': 1, 'come': 37, 'conversation': 1, 'crazy': 2, 'dance': 1, 'date': 1, 'day': 6, 'discovering': 6, 'do': 3, 'doing': 2, \"don't\": 2, 'drinking': 1, 'driver': 1, 'eat': 1, 'every': 6, 'falling': 3, 'family': 1, 'fast': 1, 'fill': 2, 'find': 1, 'first': 1, 'follow': 6, 'for': 3, 'friends': 1, 'get': 1, 'girl': 2, 'give': 1, 'go': 2, 'going': 1, 'grab': 2, 'hand': 1, 'handmade': 2, 'heart': 3, 'hours': 2, 'how': 1, 'i': 6, \"i'll\": 1, \"i'm\": 23, 'in': 27, 'is': 5, \"isn't\": 1, 'it': 1, 'jukebox': 1, 'just': 1, 'kiss': 1, 'know': 2, 'last': 3, 'lead': 6, 'leave': 1, 'let': 1, \"let's\": 2, 'like': 10, 'love': 25, 'lover': 1, 'magnet': 3, 'make': 1, 'man': 1, 'may': 2, 'me': 10, 'mind': 2, 'much': 2, 'my': 33, 'new': 6, 'night': 3, 'not': 2, 'now': 11, 'of': 6, 'okay': 1, 'on': 40, 'one': 1, 'our': 1, 'out': 1, 'over': 1, 'place': 1, 'plate': 1, 'play': 1, 'pull': 3, 'push': 3, 'put': 3, 'radio': 1, 'room': 3, 'say': 2, 'shape': 6, 'shots': 1, 'singing': 2, 'slow': 1, 'smell': 3, 'so': 2, 'somebody': 2, 'something': 6, 'sour': 1, 'start': 2, 'stop': 1, 'story': 1, 'sweet': 1, 'table': 1, 'take': 1, 'talk': 4, 'taxi': 1, 'tell': 1, 'that': 2, 'the': 18, 'then': 3, 'thrifty': 1, 'to': 2, 'too': 5, 'trust': 1, 'up': 3, 'van': 1, 'waist': 2, 'want': 2, 'was': 2, 'we': 7, \"we're\": 1, 'week': 1, 'were': 3, 'where': 1, 'with': 22, 'you': 16, 'your': 21}\n", + "True\n" + ] + } + ], + "source": [ + "# Your code her\n", + "keys = sorted(list(word_freq.keys()))\n", + "# print(keys)\n", + "word_freq2 = {key:word_freq[key] for key in keys }\n", + "print(word_freq2)\n", + "\n", + "example_output = {'a': 8, 'about': 1, 'all': 1, 'although': 3, 'and': 23, 'are': 1, 'at': 1, 'baby': 14, 'backseat': 1, 'bag': 1, 'bar': 1, 'be': 16, 'bedsheets': 3, 'begin': 1, 'best': 1, 'body': 17, 'boy': 2, 'brand': 6, 'can': 1, 'chance': 1, 'club': 1, 'come': 37, 'conversation': 1, 'crazy': 2, 'dance': 1, 'date': 1, 'day': 6, 'discovering': 6, 'do': 3, 'doing': 2, \"don't\": 2, 'drinking': 1, 'driver': 1, 'eat': 1, 'every': 6, 'falling': 3, 'family': 1, 'fast': 1, 'fill': 2, 'find': 1, 'first': 1, 'follow': 6, 'for': 3, 'friends': 1, 'get': 1, 'girl': 2, 'give': 1, 'go': 2, 'going': 1, 'grab': 2, 'hand': 1, 'handmade': 2, 'heart': 3, 'hours': 2, 'how': 1, 'i': 6, \"i'll\": 1, \"i'm\": 23, 'in': 27, 'is': 5, \"isn't\": 1, 'it': 1, 'jukebox': 1, 'just': 1, 'kiss': 1, 'know': 2, 'last': 3, 'lead': 6, 'leave': 1, 'let': 1, \"let's\": 2, 'like': 10, 'love': 25, 'lover': 1, 'magnet': 3, 'make': 1, 'man': 1, 'may': 2, 'me': 10, 'mind': 2, 'much': 2, 'my': 33, 'new': 6, 'night': 3, 'not': 2, 'now': 11, 'of': 6, 'okay': 1, 'on': 40, 'one': 1, 'our': 1, 'out': 1, 'over': 1, 'place': 1, 'plate': 1, 'play': 1, 'pull': 3, 'push': 3, 'put': 3, 'radio': 1, 'room': 3, 'say': 2, 'shape': 6, 'shots': 1, 'singing': 2, 'slow': 1, 'smell': 3, 'so': 2, 'somebody': 2, 'something': 6, 'sour': 1, 'start': 2, 'stop': 1, 'story': 1, 'sweet': 1, 'table': 1, 'take': 1, 'talk': 4, 'taxi': 1, 'tell': 1, 'that': 2, 'the': 18, 'then': 3, 'thrifty': 1, 'to': 2, 'too': 5, 'trust': 1, 'up': 3, 'van': 1, 'waist': 2, 'want': 2, 'was': 2, 'we': 7, \"we're\": 1, 'week': 1, 'were': 3, 'where': 1, 'with': 22, 'you': 16, 'your': 21}\n", + "print(word_freq2 == example_output)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Sort the values of `word_freq` ascendingly.\n", + "\n", + "Sorting the values of a dictionary is more tricky than sorting the keys because a dictionary's values are not unique. Therefore you cannot use the same way you sorted dict keys to sort dict values.\n", + "\n", + "The way to sort a dict by value is to utilize the `sorted` and `operator.itemgetter` functions. The following code snippet is provided to you to try. It will give you a list of tuples in which each tuple contains the key and value of a dict item. And the list is sorted based on the dict value ([reference](http://thomas-cokelaer.info/blog/2017/12/how-to-sort-a-dictionary-by-values-in-python/)\n", + ").\n", + "\n", + "```python\n", + "import operator\n", + "sorted_tups = sorted(word_freq.items(), key=operator.itemgetter(1))\n", + "print(sorted_tups)\n", + "```\n", + "\n", + "Therefore, the steps to sort `word_freq` by value are:\n", + "\n", + "* Using `sorted` and `operator.itemgetter`, obtain a list of tuples of the dict key-value pairs which is sorted on the value.\n", + "\n", + "* Create an empty dictionary named `word_freq2`.\n", + "\n", + "* Iterate the list of tuples. Insert each key-value pair into `word_freq2` as an object.\n", + "\n", + "Print `word_freq2` to confirm your dictionary has its values sorted. Your output should be:\n", + "\n", + "```python\n", + "{'conversation': 1, \"we're\": 1, 'plate': 1, 'sour': 1, 'jukebox': 1, 'taxi': 1, 'fast': 1, 'bag': 1, 'man': 1, 'going': 1, 'one': 1, 'backseat': 1, 'friends': 1, 'take': 1, 'play': 1, 'okay': 1, 'begin': 1, 'over': 1, 'just': 1, 'are': 1, 'tell': 1, 'drinking': 1, 'our': 1, 'where': 1, \"i'll\": 1, 'all': 1, \"isn't\": 1, 'make': 1, 'lover': 1, 'get': 1, 'radio': 1, 'give': 1, 'can': 1, 'club': 1, 'it': 1, 'out': 1, 'chance': 1, 'first': 1, 'table': 1, 'thrifty': 1, 'driver': 1, 'slow': 1, 'dance': 1, 'trust': 1, 'family': 1, 'week': 1, 'date': 1, 'leave': 1, 'at': 1, 'hand': 1, 'how': 1, 'eat': 1, 'about': 1, 'story': 1, 'sweet': 1, 'best': 1, 'let': 1, 'van': 1, 'shots': 1, 'place': 1, 'find': 1, 'kiss': 1, 'stop': 1, 'bar': 1, \"don't\": 2, 'mind': 2, 'know': 2, 'so': 2, 'start': 2, 'boy': 2, 'girl': 2, 'singing': 2, 'doing': 2, 'somebody': 2, 'handmade': 2, 'may': 2, 'that': 2, 'much': 2, 'grab': 2, 'was': 2, 'say': 2, 'waist': 2, 'want': 2, \"let's\": 2, 'not': 2, 'crazy': 2, 'go': 2, 'to': 2, 'fill': 2, 'hours': 2, 'push': 3, 'then': 3, 'put': 3, 'room': 3, 'magnet': 3, 'up': 3, 'pull': 3, 'last': 3, 'do': 3, 'smell': 3, 'although': 3, 'falling': 3, 'were': 3, 'night': 3, 'heart': 3, 'for': 3, 'bedsheets': 3, 'talk': 4, 'too': 5, 'is': 5, 'every': 6, 'new': 6, 'follow': 6, 'brand': 6, 'of': 6, 'i': 6, 'day': 6, 'lead': 6, 'shape': 6, 'discovering': 6, 'something': 6, 'we': 7, 'a': 8, 'like': 10, 'me': 10, 'now': 11, 'baby': 14, 'you': 16, 'be': 16, 'body': 17, 'the': 18, 'your': 21, 'with': 22, \"i'm\": 23, 'and': 23, 'love': 25, 'in': 27, 'my': 33, 'come': 37, 'on': 40}\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'conversation': 1, \"we're\": 1, 'plate': 1, 'sour': 1, 'jukebox': 1, 'taxi': 1, 'fast': 1, 'bag': 1, 'man': 1, 'going': 1, 'one': 1, 'backseat': 1, 'friends': 1, 'take': 1, 'play': 1, 'okay': 1, 'begin': 1, 'over': 1, 'just': 1, 'are': 1, 'tell': 1, 'drinking': 1, 'our': 1, 'where': 1, \"i'll\": 1, 'all': 1, \"isn't\": 1, 'make': 1, 'lover': 1, 'get': 1, 'radio': 1, 'give': 1, 'can': 1, 'club': 1, 'it': 1, 'out': 1, 'chance': 1, 'first': 1, 'table': 1, 'thrifty': 1, 'driver': 1, 'slow': 1, 'dance': 1, 'trust': 1, 'family': 1, 'week': 1, 'date': 1, 'leave': 1, 'at': 1, 'hand': 1, 'how': 1, 'eat': 1, 'about': 1, 'story': 1, 'sweet': 1, 'best': 1, 'let': 1, 'van': 1, 'shots': 1, 'place': 1, 'find': 1, 'kiss': 1, 'stop': 1, 'bar': 1, \"don't\": 2, 'mind': 2, 'know': 2, 'so': 2, 'start': 2, 'boy': 2, 'girl': 2, 'singing': 2, 'doing': 2, 'somebody': 2, 'handmade': 2, 'may': 2, 'that': 2, 'much': 2, 'grab': 2, 'was': 2, 'say': 2, 'waist': 2, 'want': 2, \"let's\": 2, 'not': 2, 'crazy': 2, 'go': 2, 'to': 2, 'fill': 2, 'hours': 2, 'push': 3, 'then': 3, 'put': 3, 'room': 3, 'magnet': 3, 'up': 3, 'pull': 3, 'last': 3, 'do': 3, 'smell': 3, 'although': 3, 'falling': 3, 'were': 3, 'night': 3, 'heart': 3, 'for': 3, 'bedsheets': 3, 'talk': 4, 'too': 5, 'is': 5, 'every': 6, 'new': 6, 'follow': 6, 'brand': 6, 'of': 6, 'i': 6, 'day': 6, 'lead': 6, 'shape': 6, 'discovering': 6, 'something': 6, 'we': 7, 'a': 8, 'like': 10, 'me': 10, 'now': 11, 'baby': 14, 'you': 16, 'be': 16, 'body': 17, 'the': 18, 'your': 21, 'with': 22, \"i'm\": 23, 'and': 23, 'love': 25, 'in': 27, 'my': 33, 'come': 37, 'on': 40}\n" + ] + }, + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Your code here\n", + "import operator\n", + "\n", + "sorted_tups = sorted(word_freq.items(), key=operator.itemgetter(1))\n", + "\n", + "word_freq2 = {key:value for key,value in sorted_tups }\n", + "print(word_freq2)\n", + "\n", + "word_freq == word_freq2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Convert `word_freq` into Pandas dataframes\n", + "\n", + "In your future work, you may need to convert Python dictionaries to Pandas dataframes. So let's practice this by converting `word_freq`.\n", + "\n", + "**First, import the `pandas` library.**" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "import pandas as pd" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Then, use the `pd.DataFrame()` constructor to convert `word_freq` into a dataframe.**\n", + "\n", + "Here's a [reference](https://stackoverflow.com/questions/18837262/convert-python-dict-into-a-dataframe) to show you how to accomplish this. Also name the two columns of the dataframe as `word` and `freq`.\n", + "\n", + "Assign the converted value to a variable called `df`. The first few rows of `df` should look like this:\n", + "\n", + "![df](df.png)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "ename": "ValueError", + "evalue": "If using all scalar values, you must pass an index", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m/var/folders/jm/dn5wztn13bbbr4gpvvgv4jl00000gn/T/ipykernel_11350/1541461337.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# Your code here\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mword_freq\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m/opt/anaconda3/lib/python3.9/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, data, index, columns, dtype, copy)\u001b[0m\n\u001b[1;32m 612\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 613\u001b[0m \u001b[0;31m# GH#38939 de facto copy defaults to False only in non-dict cases\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 614\u001b[0;31m \u001b[0mmgr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdict_to_mgr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtyp\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmanager\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 615\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mMaskedArray\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 616\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmrecords\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mmrecords\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/opt/anaconda3/lib/python3.9/site-packages/pandas/core/internals/construction.py\u001b[0m in \u001b[0;36mdict_to_mgr\u001b[0;34m(data, index, columns, dtype, typ, copy)\u001b[0m\n\u001b[1;32m 462\u001b[0m \u001b[0;31m# TODO: can we get rid of the dt64tz special case above?\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 463\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 464\u001b[0;31m return arrays_to_mgr(\n\u001b[0m\u001b[1;32m 465\u001b[0m \u001b[0marrays\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata_names\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtyp\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtyp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconsolidate\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 466\u001b[0m )\n", + "\u001b[0;32m/opt/anaconda3/lib/python3.9/site-packages/pandas/core/internals/construction.py\u001b[0m in \u001b[0;36marrays_to_mgr\u001b[0;34m(arrays, arr_names, index, columns, dtype, verify_integrity, typ, consolidate)\u001b[0m\n\u001b[1;32m 117\u001b[0m \u001b[0;31m# figure out the index, if necessary\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 118\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mindex\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 119\u001b[0;31m \u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_extract_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marrays\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 120\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 121\u001b[0m \u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mensure_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/opt/anaconda3/lib/python3.9/site-packages/pandas/core/internals/construction.py\u001b[0m in \u001b[0;36m_extract_index\u001b[0;34m(data)\u001b[0m\n\u001b[1;32m 623\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 624\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mindexes\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mraw_lengths\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 625\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"If using all scalar values, you must pass an index\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 626\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 627\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mhave_series\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mValueError\u001b[0m: If using all scalar values, you must pass an index" + ] + } + ], + "source": [ + "# Your code here\n", + "pd.DataFrame(word_freq)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With the Pandas DataFrame, you can sort the values easily with the built-in method `sort_values` ([reference](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.sort_values.html)).\n", + "\n", + "#### Sort `df` ascendingly based on column `word`." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# Your code here\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Sort `df` ascendingly based on column `freq`." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# Your code here\n" + ] + } + ], + "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": "3.9.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/your-code/challenge-1.ipynb b/your-code/challenge-1.ipynb index 2e59d77..1833beb 100644 --- a/your-code/challenge-1.ipynb +++ b/your-code/challenge-1.ipynb @@ -15,11 +15,12 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ - "# Your code here\n" + "# Your code here\n", + "tup = (\"I\",)" ] }, { @@ -33,11 +34,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], "source": [ - "# Your code here\n" + "# Your code here\n", + "print(type(tup))" ] }, { @@ -55,13 +65,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "AttributeError", + "evalue": "'tuple' object has no attribute 'append'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "Input \u001b[0;32mIn [3]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# Your code here\u001b[39;00m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;66;03m# list_example=[\"I\",]\u001b[39;00m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;66;03m# list_example.extend([\"r\",\"o\",\"n\",\"h\",\"a\",\"c\",\"k\"])\u001b[39;00m\n\u001b[0;32m----> 4\u001b[0m \u001b[43mtup\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mappend\u001b[49m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 5\u001b[0m tup\u001b[38;5;241m.\u001b[39mextend(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124m\"\u001b[39m,\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mo\u001b[39m\u001b[38;5;124m\"\u001b[39m,\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mn\u001b[39m\u001b[38;5;124m\"\u001b[39m,\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mh\u001b[39m\u001b[38;5;124m\"\u001b[39m,\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124ma\u001b[39m\u001b[38;5;124m\"\u001b[39m,\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mc\u001b[39m\u001b[38;5;124m\"\u001b[39m,\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mk\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", + "\u001b[0;31mAttributeError\u001b[0m: 'tuple' object has no attribute 'append'" + ] + } + ], "source": [ "# Your code here\n", - "\n", - "# Your explanation here\n" + "# list_example=[\"I\",]\n", + "# list_example.extend([\"r\",\"o\",\"n\",\"h\",\"a\",\"c\",\"k\"])\n", + "tup.append(\"r\")\n", + "tup.extend(\"r\",\"o\",\"n\",\"h\",\"a\",\"c\",\"k\")\n", + "# Your explanation here\n", + "# We cannot mutate tuples due to these data structures are immutable, which means the value inside of a tuple cannot be overwritten." ] }, { @@ -79,13 +105,33 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "('I',)\n", + "4314244384\n", + "4368773184\n", + "('I', 'r', 'o', 'n', 'h', 'a', 'c', 'k')\n" + ] + } + ], "source": [ "# Your code here\n", + "# We can reassign a tuple \n", + "print(tup)\n", + "print(id(tup))\n", + "tup=(\"I\", \"r\", \"o\", \"n\", \"h\", \"a\", \"c\", \"k\")\n", + "print(id(tup))\n", + "\n", + "# We can convert a tuple to List to mutate it and the convert it to a tuple:\n", "\n", - "# Your explanation here\n" + "# Your explanation here\n", + "print(tup)\n", + "# In the first example we are not mutating the tuple itself because the tuple is pointing to another space on memory, not the same value. We can prove it checking the ids of the variables before an after the reassignment " ] }, { @@ -103,11 +149,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "('I', 'r', 'o', 'n')\n", + "('h', 'a', 'c', 'k')\n" + ] + } + ], "source": [ - "# Your code here\n" + "# Your code here\n", + "tup1 = tup[:4]\n", + "tup2 = tup[-4:]\n", + "print(tup1)\n", + "print(tup2)" ] }, { @@ -121,11 +180,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "('I', 'r', 'o', 'n', 'h', 'a', 'c', 'k')\n", + "True\n" + ] + } + ], "source": [ - "# Your code here\n" + "# Your code here\n", + "tup3 = tup1 + tup2\n", + "print(tup3)\n", + "print(tup3==tup)" ] }, { @@ -137,11 +208,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "True\n" + ] + } + ], "source": [ - "# Your code here\n" + "# Your code here\n", + "print(len(tup1)+len(tup2) == len(tup3))" ] }, { @@ -153,11 +233,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The index of \"h\" in tup3 is 4\n" + ] + } + ], "source": [ - "# Your code here\n" + "# Your code here\n", + "print(f'The index of \"h\" in tup3 is {tup3.index(\"h\")}')" ] }, { @@ -177,11 +266,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[True, False, True, False, False]\n", + "[True, False, True, False, False]\n", + "[True, False, True, False, False]\n" + ] + } + ], "source": [ - "# Your code here\n" + "# Your code here\n", + "letters = [\"a\", \"b\", \"c\", \"d\", \"e\"]\n", + "\n", + "output_letters = [True if letter in tup3 else False for letter in letters]\n", + "\n", + "output=list(map(lambda letter: True if letter in tup3 else False, letters))\n", + "\n", + "output_1=[True if ele in tup3 else False for ele in letters]\n", + "\n", + "print(output_letters)\n", + "print(output)\n", + "print(output_1)\n" ] }, { @@ -195,17 +305,39 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'a': 1, 'b': 0, 'c': 1, 'd': 0, 'e': 0}\n" + ] + } + ], "source": [ - "# Your code here\n" + "# Your code here\n", + "\n", + "# [\"a\",\"b\", \"c\", \"a\", \"c\"]\n", + "count_letters = {letter: tup3.count(letter) for letter in letters}\n", + "\n", + "print(count_letters)\n", + "\n", + " " ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -219,7 +351,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.2" + "version": "3.8.9" } }, "nbformat": 4, diff --git a/your-code/challenge-2.ipynb b/your-code/challenge-2.ipynb index 41d6911..0062327 100644 --- a/your-code/challenge-2.ipynb +++ b/your-code/challenge-2.ipynb @@ -13,7 +13,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -38,11 +38,32 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Your code here\n" + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[12, 7, 31, 84, 47, 62, 5, 65, 4, 36, 40, 56, 2, 75, 97, 16, 27, 50, 44, 95, 9, 66, 45, 39, 98, 11, 61, 96, 42, 22, 85, 72, 76, 43, 99, 64, 17, 26, 37, 13, 74, 82, 83, 58, 0, 49, 88, 10, 51, 33, 15, 78, 34, 19, 86, 8, 57, 67, 30, 55, 69, 87, 23, 18, 79, 35, 24, 46, 63, 21, 60, 1, 20, 91, 53, 100, 73, 93, 25, 90]\n" + ] + }, + { + "data": { + "text/plain": [ + "80" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Your code here\n", + "sample_list_1 = random.sample(range(0,101), 80)\n", + "print(sample_list_1)\n", + "len(sample_list_1)" ] }, { @@ -54,11 +75,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "80" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# Your code here\n" + "# Your code here\n", + "set1 =set(sample_list_1)\n", + "len(set1)" ] }, { @@ -77,11 +111,32 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Your code here\n" + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[97, 14, 28, 64, 60, 68, 60, 15, 25, 8, 76, 10, 31, 82, 11, 66, 31, 82, 31, 62, 73, 5, 83, 52, 41, 45, 0, 35, 2, 72, 79, 4, 44, 20, 46, 96, 41, 100, 49, 64, 46, 31, 70, 46, 95, 40, 37, 13, 96, 88, 85, 83, 30, 86, 0, 77, 82, 39, 36, 27, 31, 69, 91, 90, 96, 61, 71, 44, 40, 16, 0, 40, 89, 77, 84, 87, 73, 5, 29, 91]\n", + "80\n", + "[7, 39, 79, 83, 91, 78, 66, 7, 53, 3, 86, 90, 2, 38, 97, 70, 100, 18, 70, 25, 13, 84, 25, 73, 17, 9, 54, 37, 79, 95, 47, 14, 23, 36, 98, 29, 9, 26, 20, 70, 87, 99, 37, 99, 16, 76, 85, 75, 38, 32, 44, 14, 96, 53, 77, 90, 34, 62, 92, 73, 32, 68, 56, 51, 64, 24, 18, 57, 16, 1, 54, 25, 10, 88, 61, 25, 71, 71, 26, 65]\n", + "80\n" + ] + } + ], + "source": [ + "# Your code here\n", + "sample_list_2 =[]\n", + "for _ in range(80):\n", + " # randint takes from 0 to 100\n", + " sample_list_2.append(random.randint(0,100))\n", + "\n", + "print(sample_list_2)\n", + "print(len(sample_list_2))\n", + "sample_list2_copy = [random.randint(0,100) for _ in range(80)]\n", + "print(sample_list2_copy)\n", + "print(len(sample_list2_copy))" ] }, { @@ -93,11 +148,25 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Your code here\n" + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "57" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Your code here\n", + "\n", + "set2 = set(sample_list_2)\n", + "len(set2)\n" ] }, { @@ -109,11 +178,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{1, 3, 9, 17, 18, 19, 21, 23, 24, 26, 32, 33, 38, 42, 43, 47, 48, 50, 51, 53, 54, 55, 57, 59, 63, 65, 74, 75, 78, 80, 81, 92, 94, 98, 99}\n" + ] + } + ], "source": [ - "# Your code here\n" + "# Your code here\n", + "set3= set1.difference(set2)\n", + "print(set3)\n" ] }, { @@ -125,11 +204,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{2, 70, 39, 73, 76, 13, 79, 52, 85, 86, 88, 91}\n" + ] + } + ], "source": [ - "# Your code here\n" + "# Your code here\n", + "set4 = set2.difference(set1)\n", + "print(set4)" ] }, { @@ -141,11 +230,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{0, 4, 5, 8, 10, 11, 14, 15, 16, 20, 25, 27, 28, 29, 30, 31, 35, 36, 37, 40, 41, 44, 45, 46, 49, 60, 61, 62, 64, 66, 68, 69, 71, 72, 77, 82, 83, 84, 87, 89, 90, 95, 96, 97, 100}\n" + ] + } + ], "source": [ - "# Your code here\n" + "# Your code here\n", + "set5 = set1.intersection(set2)\n", + "print(set5)" ] }, { @@ -165,11 +264,35 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Your code here\n" + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "80\n", + "57\n", + "35\n", + "12\n", + "45\n", + "True\n", + "True\n", + "False\n" + ] + } + ], + "source": [ + "# Your code here\n", + "print(len(set1))\n", + "print(len(set2))\n", + "print(len(set3))\n", + "print(len(set4))\n", + "print(len(set5))\n", + "\n", + "print(set1==set3.union(set5))\n", + "print(set4==set2.difference(set5))\n", + "print(set5==set4.union(set3))\n" ] }, { @@ -181,11 +304,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "set()\n" + ] + } + ], "source": [ - "# Your code here\n" + "# Your code here\n", + "set6=set()\n", + "print(set6)" ] }, { @@ -197,11 +330,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{0, 1, 3, 4, 5, 8, 9, 10, 11, 14, 15, 16, 17, 18, 19, 20, 21, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 35, 36, 37, 38, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 53, 54, 55, 57, 59, 60, 61, 62, 63, 64, 65, 66, 68, 69, 71, 72, 74, 75, 77, 78, 80, 81, 82, 83, 84, 87, 89, 90, 92, 94, 95, 96, 97, 98, 99, 100}\n" + ] + } + ], "source": [ - "# Your code here\n" + "# Your code here\n", + "set6.update(set3,set5)\n", + "print(set6)" ] }, { @@ -213,11 +356,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "True\n" + ] + } + ], "source": [ - "# Your code here\n" + "# Your code here\n", + "print(set1 ==set6)" ] }, { @@ -229,11 +381,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "False\n", + "True\n" + ] + } + ], "source": [ - "# Your code here\n" + "# Your code here\n", + "print(set2.issubset(set1))\n", + "print(set3.issubset(set1))" ] }, { @@ -247,11 +410,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "True\n" + ] + } + ], "source": [ - "# Your code here\n" + "# Your code here\n", + "aggregate_1=set3.union(set4, set5)\n", + "aggregate_2=set1.union(set2)\n", + "print(aggregate_1 == aggregate_2)" ] }, { @@ -263,11 +437,27 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Your code here\n" + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "79" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Your code here\n", + "set1= list (set1)\n", + "removed = set1.pop(0)\n", + "set1 = set(set1)\n", + "len(set1)\n", + " " ] }, { @@ -283,17 +473,34 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Your code here\n" + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[3, 4, 5, 8, 10, 14, 15, 16, 17, 18, 20, 23, 24, 25, 26, 27, 28, 30, 32, 33, 35, 36, 37, 38, 40, 42, 43, 44, 45, 46, 47, 48, 50, 53, 54, 55, 57, 60, 62, 63, 64, 65, 66, 68, 72, 74, 75, 77, 78, 80, 82, 83, 84, 87, 90, 92, 94, 95, 96, 97, 98, 100]\n" + ] + } + ], + "source": [ + "# Your code here\n", + "\n", + "list_to_remove = [1, 9, 11, 19, 21, 29, 31, 39, 41, 49, 51, 59, 61, 69, 71, 79, 81, 89, 91, 99]\n", + "set1 = [ele for ele in set1 if ele not in list_to_remove ]\n", + "print(set1)" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -307,7 +514,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.2" + "version": "3.8.9" } }, "nbformat": 4, diff --git a/your-code/challenge-3.ipynb b/your-code/challenge-3.ipynb index 7ab8ea5..ba30893 100644 --- a/your-code/challenge-3.ipynb +++ b/your-code/challenge-3.ipynb @@ -15,7 +15,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -49,11 +49,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'a': 8, 'about': 1, 'all': 1, 'although': 3, 'and': 23, 'are': 1, 'at': 1, 'baby': 14, 'backseat': 1, 'bag': 1, 'bar': 1, 'be': 16, 'bedsheets': 3, 'begin': 1, 'best': 1, 'body': 17, 'boy': 2, 'brand': 6, 'can': 1, 'chance': 1, 'club': 1, 'come': 37, 'conversation': 1, 'crazy': 2, 'dance': 1, 'date': 1, 'day': 6, 'discovering': 6, 'do': 3, 'doing': 2, \"don't\": 2, 'drinking': 1, 'driver': 1, 'eat': 1, 'every': 6, 'falling': 3, 'family': 1, 'fast': 1, 'fill': 2, 'find': 1, 'first': 1, 'follow': 6, 'for': 3, 'friends': 1, 'get': 1, 'girl': 2, 'give': 1, 'go': 2, 'going': 1, 'grab': 2, 'hand': 1, 'handmade': 2, 'heart': 3, 'hours': 2, 'how': 1, 'i': 6, \"i'll\": 1, \"i'm\": 23, 'in': 27, 'is': 5, \"isn't\": 1, 'it': 1, 'jukebox': 1, 'just': 1, 'kiss': 1, 'know': 2, 'last': 3, 'lead': 6, 'leave': 1, 'let': 1, \"let's\": 2, 'like': 10, 'love': 25, 'lover': 1, 'magnet': 3, 'make': 1, 'man': 1, 'may': 2, 'me': 10, 'mind': 2, 'much': 2, 'my': 33, 'new': 6, 'night': 3, 'not': 2, 'now': 11, 'of': 6, 'okay': 1, 'on': 40, 'one': 1, 'our': 1, 'out': 1, 'over': 1, 'place': 1, 'plate': 1, 'play': 1, 'pull': 3, 'push': 3, 'put': 3, 'radio': 1, 'room': 3, 'say': 2, 'shape': 6, 'shots': 1, 'singing': 2, 'slow': 1, 'smell': 3, 'so': 2, 'somebody': 2, 'something': 6, 'sour': 1, 'start': 2, 'stop': 1, 'story': 1, 'sweet': 1, 'table': 1, 'take': 1, 'talk': 4, 'taxi': 1, 'tell': 1, 'that': 2, 'the': 18, 'then': 3, 'thrifty': 1, 'to': 2, 'too': 5, 'trust': 1, 'up': 3, 'van': 1, 'waist': 2, 'want': 2, 'was': 2, 'we': 7, \"we're\": 1, 'week': 1, 'were': 3, 'where': 1, 'with': 22, 'you': 16, 'your': 21}\n", + "True\n" + ] + } + ], "source": [ - "# Your code here\n" + "# Your code her\n", + "keys = sorted(list(word_freq.keys()))\n", + "# print(keys)\n", + "word_freq2 = {key:word_freq[key] for key in keys }\n", + "print(word_freq2)\n", + "\n", + "example_output = {'a': 8, 'about': 1, 'all': 1, 'although': 3, 'and': 23, 'are': 1, 'at': 1, 'baby': 14, 'backseat': 1, 'bag': 1, 'bar': 1, 'be': 16, 'bedsheets': 3, 'begin': 1, 'best': 1, 'body': 17, 'boy': 2, 'brand': 6, 'can': 1, 'chance': 1, 'club': 1, 'come': 37, 'conversation': 1, 'crazy': 2, 'dance': 1, 'date': 1, 'day': 6, 'discovering': 6, 'do': 3, 'doing': 2, \"don't\": 2, 'drinking': 1, 'driver': 1, 'eat': 1, 'every': 6, 'falling': 3, 'family': 1, 'fast': 1, 'fill': 2, 'find': 1, 'first': 1, 'follow': 6, 'for': 3, 'friends': 1, 'get': 1, 'girl': 2, 'give': 1, 'go': 2, 'going': 1, 'grab': 2, 'hand': 1, 'handmade': 2, 'heart': 3, 'hours': 2, 'how': 1, 'i': 6, \"i'll\": 1, \"i'm\": 23, 'in': 27, 'is': 5, \"isn't\": 1, 'it': 1, 'jukebox': 1, 'just': 1, 'kiss': 1, 'know': 2, 'last': 3, 'lead': 6, 'leave': 1, 'let': 1, \"let's\": 2, 'like': 10, 'love': 25, 'lover': 1, 'magnet': 3, 'make': 1, 'man': 1, 'may': 2, 'me': 10, 'mind': 2, 'much': 2, 'my': 33, 'new': 6, 'night': 3, 'not': 2, 'now': 11, 'of': 6, 'okay': 1, 'on': 40, 'one': 1, 'our': 1, 'out': 1, 'over': 1, 'place': 1, 'plate': 1, 'play': 1, 'pull': 3, 'push': 3, 'put': 3, 'radio': 1, 'room': 3, 'say': 2, 'shape': 6, 'shots': 1, 'singing': 2, 'slow': 1, 'smell': 3, 'so': 2, 'somebody': 2, 'something': 6, 'sour': 1, 'start': 2, 'stop': 1, 'story': 1, 'sweet': 1, 'table': 1, 'take': 1, 'talk': 4, 'taxi': 1, 'tell': 1, 'that': 2, 'the': 18, 'then': 3, 'thrifty': 1, 'to': 2, 'too': 5, 'trust': 1, 'up': 3, 'van': 1, 'waist': 2, 'want': 2, 'was': 2, 'we': 7, \"we're\": 1, 'week': 1, 'were': 3, 'where': 1, 'with': 22, 'you': 16, 'your': 21}\n", + "print(word_freq2 == example_output)" ] }, { @@ -90,11 +106,37 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'conversation': 1, \"we're\": 1, 'plate': 1, 'sour': 1, 'jukebox': 1, 'taxi': 1, 'fast': 1, 'bag': 1, 'man': 1, 'going': 1, 'one': 1, 'backseat': 1, 'friends': 1, 'take': 1, 'play': 1, 'okay': 1, 'begin': 1, 'over': 1, 'just': 1, 'are': 1, 'tell': 1, 'drinking': 1, 'our': 1, 'where': 1, \"i'll\": 1, 'all': 1, \"isn't\": 1, 'make': 1, 'lover': 1, 'get': 1, 'radio': 1, 'give': 1, 'can': 1, 'club': 1, 'it': 1, 'out': 1, 'chance': 1, 'first': 1, 'table': 1, 'thrifty': 1, 'driver': 1, 'slow': 1, 'dance': 1, 'trust': 1, 'family': 1, 'week': 1, 'date': 1, 'leave': 1, 'at': 1, 'hand': 1, 'how': 1, 'eat': 1, 'about': 1, 'story': 1, 'sweet': 1, 'best': 1, 'let': 1, 'van': 1, 'shots': 1, 'place': 1, 'find': 1, 'kiss': 1, 'stop': 1, 'bar': 1, \"don't\": 2, 'mind': 2, 'know': 2, 'so': 2, 'start': 2, 'boy': 2, 'girl': 2, 'singing': 2, 'doing': 2, 'somebody': 2, 'handmade': 2, 'may': 2, 'that': 2, 'much': 2, 'grab': 2, 'was': 2, 'say': 2, 'waist': 2, 'want': 2, \"let's\": 2, 'not': 2, 'crazy': 2, 'go': 2, 'to': 2, 'fill': 2, 'hours': 2, 'push': 3, 'then': 3, 'put': 3, 'room': 3, 'magnet': 3, 'up': 3, 'pull': 3, 'last': 3, 'do': 3, 'smell': 3, 'although': 3, 'falling': 3, 'were': 3, 'night': 3, 'heart': 3, 'for': 3, 'bedsheets': 3, 'talk': 4, 'too': 5, 'is': 5, 'every': 6, 'new': 6, 'follow': 6, 'brand': 6, 'of': 6, 'i': 6, 'day': 6, 'lead': 6, 'shape': 6, 'discovering': 6, 'something': 6, 'we': 7, 'a': 8, 'like': 10, 'me': 10, 'now': 11, 'baby': 14, 'you': 16, 'be': 16, 'body': 17, 'the': 18, 'your': 21, 'with': 22, \"i'm\": 23, 'and': 23, 'love': 25, 'in': 27, 'my': 33, 'come': 37, 'on': 40}\n" + ] + }, + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# Your code here\n" + "# Your code here\n", + "import operator\n", + "\n", + "sorted_tups = sorted(word_freq.items(), key=operator.itemgetter(1))\n", + "\n", + "word_freq2 = {key:value for key,value in sorted_tups }\n", + "print(word_freq2)\n", + "\n", + "word_freq == word_freq2" ] }, { @@ -110,8 +152,10 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, + "execution_count": 9, + "metadata": { + "scrolled": true + }, "outputs": [], "source": [ "import pandas as pd" @@ -132,11 +176,120 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
wordfreq
0love25
1conversation1
2every6
3we're1
4plate1
.........
135bedsheets3
136fill2
137hours2
138stop1
139bar1
\n", + "

140 rows × 2 columns

\n", + "
" + ], + "text/plain": [ + " word freq\n", + "0 love 25\n", + "1 conversation 1\n", + "2 every 6\n", + "3 we're 1\n", + "4 plate 1\n", + ".. ... ...\n", + "135 bedsheets 3\n", + "136 fill 2\n", + "137 hours 2\n", + "138 stop 1\n", + "139 bar 1\n", + "\n", + "[140 rows x 2 columns]" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# Your code here\n" + "df=pd.DataFrame(word_freq.items(), columns =[ \"word\", \"freq\"])\n", + "df" ] }, { @@ -150,11 +303,120 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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wordfreq
120a8
109about1
43all1
96although3
72and23
.........
128were3
41where1
54with22
15you16
97your21
\n", + "

140 rows × 2 columns

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" + ], + "text/plain": [ + " word freq\n", + "120 a 8\n", + "109 about 1\n", + "43 all 1\n", + "96 although 3\n", + "72 and 23\n", + ".. ... ...\n", + "128 were 3\n", + "41 where 1\n", + "54 with 22\n", + "15 you 16\n", + "97 your 21\n", + "\n", + "[140 rows x 2 columns]" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# Your code here\n" + "# Your code here\n", + "df.sort_values(by=[\"word\"])" ] }, { @@ -166,17 +428,126 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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.........
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140 rows × 2 columns

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" + ], + "text/plain": [ + " word freq\n", + "139 bar 1\n", + "114 let 1\n", + "116 van 1\n", + "60 out 1\n", + "57 it 1\n", + ".. ... ...\n", + "0 love 25\n", + "65 in 27\n", + "121 my 33\n", + "56 come 37\n", + "126 on 40\n", + "\n", + "[140 rows x 2 columns]" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# Your code here\n" + "# Your code here\n", + "df.sort_values(by=[\"freq\"])" ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -190,7 +561,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.2" + "version": "3.9.7" } }, "nbformat": 4,