machine-learning-course-uac/0-intro-jupyter/main.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "79908dc6",
   "metadata": {},
   "source": [
    "# How Jupyter works"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "408602ca",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'x' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn [1], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[43mx\u001b[49m)\n",
      "\u001b[0;31mNameError\u001b[0m: name 'x' is not defined"
     ]
    }
   ],
   "source": [
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "598f5bed",
   "metadata": {},
   "outputs": [],
   "source": [
    "x=3"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77d75f52",
   "metadata": {},
   "source": [
    "## Basic commands\n",
    "\n",
    "Jupyter Notebook is widely used for data analysis. The notebooks documents are documents produced by the Jupyter Notebook App, which can contain both code (e.g. Python) and rich text elements (paragraphs, equations, links, etc.).\n",
    "\n",
    "The Jupyter Notebook App is a client-server application that allows editing and running notebook documents by a browser.\n",
    "\n",
    "### Shortcuts in both modes:\n",
    "- Shift + Enter run the current cell, select below\n",
    "- Ctrl + Enter run selected cells\n",
    "- Alt + Enter run the current cell, insert below\n",
    "- Ctrl + S save and checkpoint"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aea23ddf",
   "metadata": {},
   "source": [
    "### While in command mode (press Esc to activate):\n",
    "\n",
    "**Enter take you into edit mode** and also **considers that some commands en Google collab have been modified and require first the `Ctr+M` command** and then the command that you need like change the cell type to Markdown:\n",
    "\n",
    "- H show all shortcuts `Ctr+M`\n",
    "- Up select cell above\n",
    "- Down select cell below\n",
    "- Shift + Up extend selected cells above\n",
    "- Shift + Down extend selected cells below\n",
    "- A insert cell above\n",
    "- B insert cell below\n",
    "- X cut selected cells\n",
    "- C copy selected cells\n",
    "- V paste cells below\n",
    "- Shift + V paste cells above\n",
    "- D, D (press the key twice) delete selected cells `Ctr+M`\n",
    "- Z undo cell deletion\n",
    "- S Save and Checkpoint `Ctr+M`\n",
    "- Y change the cell type to Code `Ctr+M` \n",
    "- M change the cell type to Markdown `Ctr+M`\n",
    "- P open the command palette `Ctr+M`\n",
    "\n",
    "This dialog helps you run any command by name. It’s really useful if you don’t know some shortcut or when you don’t have a shortcut for the wanted command.\n",
    "\n",
    "- Shift + Space scroll notebook up\n",
    "- Space scroll notebook down"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6066c3fc",
   "metadata": {},
   "source": [
    "### While in edit mode (press Enter to activate)\n",
    "- Esc take you into command mode\n",
    "- Tab code completion or indent\n",
    "- Shift + Tab tooltip\n",
    "- Ctrl + ] indent\n",
    "- Ctrl + [ dedent\n",
    "- Ctrl + A select all\n",
    "- Ctrl + Z undo\n",
    "- Ctrl + Shift + Z or Ctrl + Y redo\n",
    "- Ctrl + Home go to cell start\n",
    "- Ctrl + End go to cell end\n",
    "- Ctrl + Left go one word left\n",
    "- Ctrl + Right go one word right\n",
    "- Ctrl + Shift + P open the command palette\n",
    "- Down move cursor down\n",
    "- Up move cursor up"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dad4c1fb",
   "metadata": {},
   "source": [
    "# Basics of Python and Programming\n",
    "## Using conditionals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9272c3fd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "El valor es positivo\n"
     ]
    }
   ],
   "source": [
    "x = 1 #int(input('Introduce un numero: '))\n",
    "if x<0:\n",
    "    print('El valor es negativo')\n",
    "else:\n",
    "    print(\"El valor es positivo\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "90916e74",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Introduce un numero: -5\n",
      "El valor es negativo\n"
     ]
    }
   ],
   "source": [
    "x = int(input('Introduce un numero: '))\n",
    "if x<0:\n",
    "    print('El valor es negativo')\n",
    "else:\n",
    "    print(\"El valor es positivo\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e246b55e",
   "metadata": {},
   "source": [
    "## Using for loops"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7d68db25",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "juan\n",
      "israel\n",
      "jonathan\n",
      "homero\n"
     ]
    }
   ],
   "source": [
    "nombres = ['juan', 'israel', 'jonathan', 'homero']\n",
    "for n in nombres:\n",
    "    print(n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b5a09022",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n"
     ]
    }
   ],
   "source": [
    "for n in range(1,11):\n",
    "    print(n)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e163a7e0",
   "metadata": {},
   "source": [
    "## Arrays and indexing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d98ef5bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "a = np.array([[1,2],[3,4],[5,6]])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "096d51b8",
   "metadata": {},
   "source": [
    "## Functions "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "fd363297",
   "metadata": {},
   "outputs": [],
   "source": [
    "# how to crate functions\n",
    "def myFuncion(x):\n",
    "    y = np.sin(x)/np.cos(x)\n",
    "    return y"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8fada9a",
   "metadata": {},
   "source": [
    "## Making basic plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b956d7c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "time = np.linspace(0,10)\n",
    "y = myFuncion(time)\n",
    "y2 = np.tan(time)\n",
    "plt.plot(time, y/2,'-g*')\n",
    "plt.plot(time, y2, ':m+')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "280bf852",
   "metadata": {},
   "source": [
    "### Subplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "048067ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 640x480 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(3,1,1)\n",
    "plt.plot(time, np.sin(time),'r', label='$y_1$')\n",
    "plt.legend()\n",
    "plt.subplot(3,1,2)\n",
    "plt.plot(time, np.cos(time),'g',label='y2')\n",
    "plt.legend()\n",
    "plt.subplot(3,1,3)\n",
    "plt.plot(time, np.tan(time),'k', label='y3')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4be8971b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 640x480 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(6,3,1)\n",
    "plt.plot(time, np.sin(time),'r', label='y_1')\n",
    "plt.subplot(6,3,2)\n",
    "plt.plot(time, np.cos(time),'g',label='y2')\n",
    "plt.subplot(6,3,3)\n",
    "plt.plot(time, np.tan(time),'k', label='y3')\n",
    "plt.subplot(6,3,4)\n",
    "plt.plot(time, np.sin(time),'r', label='$y_1$')\n",
    "plt.subplot(6,3,5)\n",
    "plt.plot(time, np.cos(time),'g',label='y2')\n",
    "plt.subplot(6,3,6)\n",
    "plt.plot(time, np.tan(time),'k', label='$y_1$')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8236e97a",
   "metadata": {},
   "source": [
    "# Some additions task and work"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "937e9070",
   "metadata": {},
   "source": [
    "- Make function that computes the factorial of a given numer using for loops\n",
    "- Make a function that computes 4 functions (sin, cos, tan, logistic) and plots \n",
    "- Make a function that recive your age and classifies you as baby, teenager, ...."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f60bf212",
   "metadata": {},
   "source": [
    "## factorial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2eee578a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def factorial(argument):\n",
    "    mul=1\n",
    "    for n in range(1,argument):\n",
    "        prod = mul*(n+1)\n",
    "        mul = prod\n",
    "        print(prod)\n",
    "    pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9c9addfe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n",
      "6\n",
      "24\n",
      "120\n",
      "720\n",
      "5040\n"
     ]
    }
   ],
   "source": [
    "factorial(7)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "841a2854",
   "metadata": {},
   "source": [
    "## making plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d19216c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "def myPlots(x):\n",
    "    y1 = np.sin(x)\n",
    "    y2 = np.cos(x)\n",
    "    y3 = np.tan(x)\n",
    "    y4 = 1/(1+np.exp(-x))\n",
    "    #plots\n",
    "    plt.figure(figsize=(10,10))\n",
    "    plt.subplot(4,4,1)\n",
    "    plt.plot(x, y1)\n",
    "    plt.subplot(4,4,2)\n",
    "    plt.plot(x, y2)\n",
    "    plt.subplot(4,4,3)\n",
    "    plt.plot(x, y3)\n",
    "    plt.subplot(4,4,4)\n",
    "    plt.plot(x, y4)\n",
    "    pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "322fae49",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 1000x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(-10,10)\n",
    "myPlots(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1637262f",
   "metadata": {},
   "source": [
    "## Age classificator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "25857354",
   "metadata": {},
   "outputs": [],
   "source": [
    "def AgeClassificator(age):\n",
    "    if age<=2:\n",
    "        print('You are a baby')\n",
    "    else: \n",
    "        if age <=12:\n",
    "            print('you are a children')\n",
    "        else:\n",
    "            if age<=18:\n",
    "                print('you are a teenager')\n",
    "            else:\n",
    "                if age <=40:\n",
    "                    print('you are young')\n",
    "                else:\n",
    "                    print('You are old-younger')\n",
    "    pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "744b17f9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "You are old-younger\n"
     ]
    }
   ],
   "source": [
    "AgeClassificator(41)"
   ]
  }
 ],
 "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": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}