{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lab 3\n",
    "\n",
    "This lab's purpose is to analyze Steve Jobs's speech \"Stay Hungry. Stay Foolish.\"\n",
    "\n",
    " 1. Read the file. The file may be stored in a local folder on the internet.\n",
    " 2. What is the type of data stored in the variable speech? \n",
    " 3. Remove symbols that are not useful. Suggestion: use replace method. \n",
    " 4. Convert the string into a list.\n",
    " 5. Use a dictionary to get the list of words and their frequencies.\n",
    " 6. Sort the dictionary alphabetically.\n",
    " 7. How many words does the text have?\n",
    " 8. What is the most frequent word?\n",
    " 9. Sort the words from the most frequent to the least frequent.\n",
    " 10. How many words have appeared more than 50 times in the text?\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " **1.** Read the file. The file may be stored in a local folder on in the internet."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "#url='https://raw.githubusercontent.com/masterfloss/data/main/jobs.txt'\n",
    "url='https://raw.githubusercontent.com/masterfloss/text/main/jobs.txt'\n",
    "response = requests.get(url)\n",
    "speech = response.text\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# an alternative if your file is stored in the current folder\n",
    "#f = open(\"jobs.txt\", \"r\")\n",
    "#speech=f.read()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " **2.**  What is the type of data stored into the variable speech. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " **3.**  Remove simbols that are not useful. Sugestion: use replace method. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**4.**  Convert the string into a list."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**5.**  Use a dictionary to get the list of words and their frequencies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**6.**  Sort the dictionary alphabetically."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**7.**  How many words does the text have?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**8.**  What is the most frequent word?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**9.** Sort the words from the most frequent to the least frequent."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**10.** How many words have appeared more than 50 times in the text?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#\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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}