One-Liner.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "fea7584c",
   "metadata": {},
   "source": [
    "## TABLE OF CONTENTS\n",
    "\n",
    "* [USING LIST COMPREHENSION TO FIND TOP EARNERS](#first-bullet)\n",
    "* [USING LIST COMPREHENSION TO FIND WORDS WITH HIGH INFORMATION VALUE](#second-bullet)\n",
    "* [READING A FILE](#third-bullet)\n",
    "* [USING LAMBDA AND MAP FUNCTION](#forth-bullet)\n",
    "* [USING SLICING TO EXTRACT MATCHING SUBSTRING ENVIROMENTS](#fifth-bullet)\n",
    "* [COMBINING LIST COMPREHENSION AND SLICING](#sixth-bullet)\n",
    "* [USING SLICE ASSIGNMENT TO CORRECT CORRUPTED LISTS](#seventh-bullet)\n",
    "* [ANALYZING CARDIAC HEALTH DATA WITH LIST CONCATENATION](#eighth-bullet)\n",
    "* [USING GENERATOR EXPRESSIONS TO FIND COMPANIES THAT PAY BELOW MINIMUM WAGE](#nineth-bullet)\n",
    "* [FORMATTING DATABASES WITH THE ZIP() FUNCTION](#tenth-bullet)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06f65173",
   "metadata": {},
   "source": [
    "## List Comprehension\n",
    "\n",
    "    [expresion + context]\n",
    "\n",
    "```{python}\n",
    "[x * 2 for x in range(3)]\n",
    "\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "686e9bb7",
   "metadata": {},
   "source": [
    "#### USING LIST COMPREHENSION TO FIND TOP EARNERS  <a class=\"anchor\" id=\"first-bullet\"><a/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "70ef6fca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('Alice', 100000), ('Carol', 122908)]\n"
     ]
    }
   ],
   "source": [
    "#find top earners\n",
    "employees = {\n",
    "    'Alice':100000,\n",
    "    'Bob':99817,\n",
    "    'Carol':122908,\n",
    "    'Frank':88123,\n",
    "    'Eve':93121\n",
    "}\n",
    "\n",
    "print([(k,v) for k,v in employees.items() if v >= 100000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6df75228",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(0, 0), (0, 1), (0, 2), (1, 0), (1, 1), (1, 2), (2, 0), (2, 1), (2, 2)]\n"
     ]
    }
   ],
   "source": [
    "#other example\n",
    "print([(x, y) for x in range(3) for y in range(3)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "faf594b2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 9, 25, 49, 81]\n"
     ]
    }
   ],
   "source": [
    "#other example\n",
    "print([x ** 2 for x in range(10) if x % 2 > 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f76f117c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['i', 'am', 'not', 'shouting']\n"
     ]
    }
   ],
   "source": [
    "#other example\n",
    "print([x.lower() for x in ['I', 'AM', 'NOT', 'SHOUTING']])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5630e7aa",
   "metadata": {},
   "source": [
    "#### USING LIST COMPREHENSION TO FIND WORDS WITH HIGH INFORMATION VALUE <a class=\"anchor\" id=\"second-bullet\"><a/>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "015041a3",
   "metadata": {},
   "source": [
    "*Given a multiline string, create a list of a lists: each consiting of all the words in a line that have more that three characters.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f593df3c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[['Call', 'Ishmael.', 'Some', 'year', 'never', 'mind', 'long', 'precisely', 'having'], ['little', 'money', 'purse,', 'nothing', 'particular', 'interest'], ['shore,', 'thought', 'would', 'sail', 'about', 'little', 'watery', 'part'], ['world.', 'have', 'driving', 'spleen,', 'regulating'], ['circulation.', 'Moby', 'Dick']]\n"
     ]
    }
   ],
   "source": [
    "#Data\n",
    "text = '''Call me Ishmael. Some year ago - never mind how long precisely - having\n",
    "little or no money in my purse, and nothing particular to interest me\n",
    "on shore, I thought I would sail about a little and see the watery part\n",
    "of the world. It is a way I have of driving off the spleen, and regulating\n",
    "the circulation. - Moby Dick'''\n",
    "\n",
    "#One-liner\n",
    "w = [[x for x in line.split() if len(x) >3] for line in text.split('\\n')]\n",
    "\n",
    "#Result\n",
    "print(w)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3a5037b",
   "metadata": {},
   "source": [
    "#### READING A FILE <a class=\"anchor\" id=\"third-bullet\"><a/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0c237a4d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['filename = r\"readFiles.py\" #this code', '', 'f = open(filename)', 'lines = []', 'for line in f:', 'lines.append(line.strip())', '', 'print(lines)', '', '#the one-liner would be: print([line.strip() for line in open(r\"readFiles.py\")])']\n"
     ]
    }
   ],
   "source": [
    "#another example\n",
    "print([line.strip() for line in open(r\"C:\\PythonFundamentos\\zero-to-mastery\\Py_Programs\\readFiles.py\")])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e84ad26",
   "metadata": {},
   "source": [
    "#### USING LAMBDA AND MAP FUNCTIONS <a class=\"anchor\" id=\"forth-bullet\"><a/>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "400a48fa",
   "metadata": {},
   "source": [
    "*Given a list of strings, our next one-liner creates a new list of tuples, each consisting of a Boolean value and the original string. The boolean value will apply according to a condition*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c6fd7fec",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(True, 'lambda function are anonymous functions.'), (True, 'anonymous functions dont have a name.'), (False, 'functions are objects in Python.')]\n"
     ]
    }
   ],
   "source": [
    "#Data\n",
    "txt = ['lambda function are anonymous functions.',\n",
    "      'anonymous functions dont have a name.',\n",
    "      'functions are objects in Python.']\n",
    "\n",
    "#One-Liner\n",
    "mark = map(lambda s: (True, s) if 'anonymous' in s else (False, s), txt)\n",
    "\n",
    "#Result\n",
    "print(list(mark))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "88c06405",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(True, 'lambda function are anonymous functions.'), (True, 'anonymous functions dont have a name.'), (False, 'functions are objects in Python.')]\n"
     ]
    }
   ],
   "source": [
    "#one-liner to substitute the map()\n",
    "print([(True, x) if 'anonymous' in x else (False, x) for x in txt])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa106d41",
   "metadata": {},
   "source": [
    "#### USING SLICING TO EXTRACT MATCHING SUBSTRING ENVIROMENTS <a class=\"anchor\" id=\"fifth-bullet\"><a/>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28670109",
   "metadata": {},
   "source": [
    "*Given a text, we'll find an specifi word within it's enviroment (18 characters before and 18 characters after)*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "001cb7c3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a fully-managed MySQL and PostgreSQL\n"
     ]
    }
   ],
   "source": [
    "#Data\n",
    "letters_amazon = '''\n",
    "We spent several years building our own database engine,\n",
    "Amazon Aurora, a fully-managed MySQL and PostgreSQL-compatible\n",
    "service with the same or better durability and availability as\n",
    "the commercial engines, but at one-tenth of the cost.\n",
    "We were bot surprised when this worked.'''\n",
    "\n",
    "#One-Liner\n",
    "find = lambda x, q: x[x.find(q)-18:x.find(q)+18] if q in x else -1\n",
    "\n",
    "#Result\n",
    "print(find(letters_amazon, 'SQL'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68aa70f4",
   "metadata": {},
   "source": [
    "#### COMBINING LIST COMPREHENSION AND SLICING <a class=\"anchor\" id=\"sixth-bullet\"><a/>\n",
    "\n",
    "- List comprehension iterates over each list element and modifies\n",
    "- Slicing selects every other element from a given list quickly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e5eb7c4d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[9.9, 9.8, 9.5], [9.5, 9.4, 9.2], [8.4, 7.9, 8.0], [7.1, 4.8, 4.7]]\n"
     ]
    }
   ],
   "source": [
    "# Data (daily stock prices($))\n",
    "price = [\n",
    "    [9.9, 9.8, 9.8, 9.4, 9.5, 9.7],\n",
    "    [9.5, 9.4, 9.4, 9.3, 9.2, 9.1],\n",
    "    [8.4, 7.9, 7.9, 8.1, 8.0, 8.0],\n",
    "    [7.1, 5.9, 4.8, 4.8, 4.7, 3.9]\n",
    "]\n",
    "\n",
    "# One-liner\n",
    "sample = [line[::2] for line in price]\n",
    "\n",
    "# Result\n",
    "print(sample)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "515348f0",
   "metadata": {},
   "source": [
    "#### SLICE ASSIGMENT TO CORRECT CORRUPTED <a class=\"anchor\" id=\"seventh-bullet\"><a/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "96950357",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Firefox', 'Firefox', 'Chrome', 'Chrome', 'Safari', 'Safari', 'Safari', 'Safari', 'Chrome', 'Chrome', 'Firefox', 'Firefox']\n"
     ]
    }
   ],
   "source": [
    "# Data\n",
    "visitors = [\n",
    "    'Firefox', 'corrupted', 'Chrome', 'corrupted',\n",
    "    'Safari', 'corrupted', 'Safari', 'corrupted',\n",
    "    'Chrome', 'corrupted', 'Firefox', 'corrupted'\n",
    "]\n",
    "\n",
    "# One-liner\n",
    "visitors[1::2] = visitors[::2]\n",
    "\n",
    "# Result\n",
    "print(visitors)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a370f6f9",
   "metadata": {},
   "source": [
    "#### ANALYZING CARDIAC HEALTH DATA WITH LIST CONCATENATION <a class=\"anchor\" id=\"eighth-bullet\"><a/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "12333976",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3, 1, 2, 3, 1, 2, 3]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test = [1,2,3]\n",
    "test * 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "db30b71b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Dependencies\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Data\n",
    "cardiac_cycle = [62, 60, 62, 64, 68, 77, 80, 76, 71, 66, 61, 60, 62]\n",
    "\n",
    "# One-liner\n",
    "expected_cycles = cardiac_cycle[1:-2] * 10\n",
    "\n",
    "# Result\n",
    "plt.plot(expected_cycles)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2b99c45",
   "metadata": {},
   "source": [
    "#### USING GENERATOR EXPRESSIONS TO FIND COMPANIES THAT PAY BELOW MINIMUM WAGE <a class=\"anchor\" id=\"nineth-bullet\"><a/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "17a62fd7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "in ex1 the result is: True because one element is True, but in ex2 all are: False.\n"
     ]
    }
   ],
   "source": [
    "# any() iterates over an object an returns True if one element is True\n",
    "\n",
    "ex1 = any([False, True, False, False])\n",
    "ex2 = any([2<1, 3+2>5+5, 3-2<0, 0])\n",
    "\n",
    "print(f'in ex1 the result is: {ex1} because one element is True, but in ex2 all are: {ex2}.')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa085195",
   "metadata": {},
   "source": [
    "Generating Expressions works exactly like list comprehension, but doesn't create an actual list in memory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "229780f4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This is a list comprehension example: 2470. \n",
      "\n",
      "But this is a generator expresion: sum(x*x for x in range(20)), which results is: 2470\n"
     ]
    }
   ],
   "source": [
    "ex3 = sum([x*x for x in range(20)])\n",
    "print(f'''This is a list comprehension example: {ex3}. \n",
    "\n",
    "But this is a generator expresion: sum(x*x for x in range(20)), which results is: {sum(x*x for x in range(20))}''')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "81111d25",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['CheapCompany', 'SosoCompany']\n"
     ]
    }
   ],
   "source": [
    "# Data\n",
    "companies = {\n",
    "    'CoolCompany' : {'Alice' : 33, 'Bob' : 28, 'Frank' : 29},\n",
    "    'CheapCompany' : {'Ann' : 4, 'Lee' : 9, 'Chrisi' : 7},\n",
    "    'SosoCompany' : {'Esther' : 38, 'Cole' : 8, 'Paris' : 18}\n",
    "}\n",
    "\n",
    "# One-liner\n",
    "illegal = [x for x in companies if any(y<9 for y in companies[x].values())]\n",
    "\n",
    "# Result\n",
    "print(illegal)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b22e426",
   "metadata": {},
   "source": [
    "#### FORMATTING DATABASES WITH THE ZIP() FUNCTION <a class=\"anchor\" id=\"tenth-bullet\"><a/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "d14b6f95",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(1, 4), (2, 5), (3, 6)]\n",
      "[1, 2, 3]\n",
      "[4, 5, 6]\n"
     ]
    }
   ],
   "source": [
    "lst1 = [1, 2, 3]\n",
    "lst2 = [4, 5, 6]\n",
    "\n",
    "# Zip two lists together\n",
    "zipped = list(zip(lst1, lst2))\n",
    "print(zipped)\n",
    "\n",
    "# Unzip to list again\n",
    "lst_1_new, lst_2_new = zip(*zipped) #* unpacks all element of the list by removing the outer bracket\n",
    "print(list(lst_1_new))\n",
    "print(list(lst_2_new))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "b37d768f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[{'name': 'Alice', 'salary': 180000, 'job': 'data scientist'}, {'name': 'Bob', 'salary': 99000, 'job': 'mid-level manager'}, {'name': 'Frank', 'salary': 87000, 'job': 'CEO'}]\n"
     ]
    }
   ],
   "source": [
    "# Data\n",
    "column_names = ['name', 'salary', 'job']\n",
    "db_rows = [\n",
    "    ('Alice', 180000, 'data scientist'),\n",
    "    ('Bob', 99000, 'mid-level manager'),\n",
    "    ('Frank', 87000, 'CEO')\n",
    "]\n",
    "\n",
    "# One-liner \n",
    "db = [dict(zip(column_names, row)) for row in db_rows]\n",
    "\n",
    "# Result\n",
    "print(db)"
   ]
  }
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