{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a href=\"https://colab.research.google.com/github/visiont3lab/tecnologie_data_science/blob/master/book/docs/programming/google_colab.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "oZjUvODbqhaC"
   },
   "source": [
    "# Google Colaboratory"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "O4xiNThbqxY4"
   },
   "source": [
    "Uno Jupyter notebook è un insieme di celle che permettono sia di annotare informazioni (Celle di Testo) che di scrivere codice (Celle di Codice ). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "6QXcdFfbrhsK"
   },
   "source": [
    "## Text Cell \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "eic-gYY7viEU"
   },
   "source": [
    "Una cella di testo è una cella utilizzata per annotare appunti in modo semplice e veloce. Al fine di annotare velocemente informazioni si utilizza un astrazione del linguaggio html chiamato Markdown. Questo linguaggio permette velocemnte  e in modo poco verbose di scrivere codice html. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "aiO2PUKjsQrZ"
   },
   "source": [
    "### Markdown \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "MKpimnpTvmOm"
   },
   "source": [
    "Riassumiamo le principali caratteristiche del linguaggio Markdown che utilizzeremo per scrivere appunti nei nostri notebook.\n",
    "\n",
    "Maggiori informazioni sul linguaggio Markdown si posso trovare leggendo [Markdown Guide](https://colab.research.google.com/notebooks/markdown_guide.ipynb) e l'articolo [Mastering Markdown](https://guides.github.com/features/mastering-markdown/)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "RhLSUwhPtjzm"
   },
   "source": [
    "#### Headers\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "xWWtTmM1vpx-"
   },
   "source": [
    "> \n",
    "    # h1\n",
    "    ## h2\n",
    "    ### h3\n",
    "    #### h4\n",
    "    #### h5\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "mOv-rVVfu_Q3"
   },
   "source": [
    "#### Bold e Italic\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "NWs9RM85vsRG"
   },
   "source": [
    "> **bold text** , *italicized text* ~~strikethrough~~\n",
    "> \n",
    "    **bold text** , *italicized text* , ~~strikethrough~~\n",
    "\n",
    "\n",
    "    \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "UFaEWX_gvxZW"
   },
   "source": [
    "#### Blockquote\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "-QyRQUKiwbDE"
   },
   "source": [
    "\n",
    "> 1 blockquote\n",
    ">> 2 blockquote\n",
    "\n",
    "\n",
    "> \n",
    "    > 1 blockquote\n",
    "    >> 2 blockquote\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "UHXClGV2whAN"
   },
   "source": [
    "#### Liste"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "jvAdkhsmwdwA"
   },
   "source": [
    "Ordinate\n",
    "1. First item\n",
    "2. Second item\n",
    "3. Third item\n",
    "\n",
    "---\n",
    "\n",
    "Non ordinate\n",
    "> \n",
    "    - First item\n",
    "    - Second item\n",
    "    - Third item\n",
    "\n",
    "\n",
    "- First item\n",
    "- Second item\n",
    "- Third item\n",
    "\n",
    "Lista di Task da fare\n",
    ">\n",
    "    - [x] Write the press release\n",
    "    - [ ] Update the website\n",
    "    - [ ] Contact the media\n",
    "\n",
    "- [x] Write the press release\n",
    "- [ ] Update the website\n",
    "- [ ] Contact the media\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "gl2Wvji42X8V"
   },
   "source": [
    "#### Latex Math formula\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "mSqBaT7v0DyP"
   },
   "source": [
    "\n",
    "> \n",
    "    1. $\\sqrt{3x-1}+(1+x)^2.$\n",
    "    2. $y=x^2$\n",
    "    3. $e^{i\\pi} + 1 = 0$\n",
    "    4. $e^x=\\sum_{i=0}^\\infty \\frac{1}{i!}x^i$\n",
    "    5. $\\frac{n!}{k!(n-k)!} = {n \\choose k}$\n",
    "    6. $A_{m,n} =\\begin{pmatrix}\n",
    "                  a_{1,1} & a_{1,2} & \\cdots & a_{1,n} \\\\\n",
    "                  a_{2,1} & a_{2,2} & \\cdots & a_{2,n} \\\\\n",
    "                  \\vdots  & \\vdots  & \\ddots & \\vdots  \\\\\n",
    "                  a_{m,1} & a_{m,2} & \\cdots & a_{m,n}\n",
    "                 \\end{pmatrix}$\n",
    "\n",
    "\n",
    "> 1) $\\sqrt{3x-1}+(1+x)^2.$\n",
    "\n",
    "> 2) $y=x^2$\n",
    "\n",
    "> 3) $e^{i\\pi} + 1 = 0$\n",
    "\n",
    "> 4) $e^x=\\sum_{i=0}^\\infty \\frac{1}{i!}x^i$\n",
    "\n",
    "> 5) $\\frac{n!}{k!(n-k)!} = {n \\choose k}$\n",
    "\n",
    "> 6) $A_{m,n} =\n",
    " \\begin{pmatrix}\n",
    "  a_{1,1} & a_{1,2} & \\cdots & a_{1,n} \\\\\n",
    "  a_{2,1} & a_{2,2} & \\cdots & a_{2,n} \\\\\n",
    "  \\vdots  & \\vdots  & \\ddots & \\vdots  \\\\\n",
    "  a_{m,1} & a_{m,2} & \\cdots & a_{m,n}\n",
    " \\end{pmatrix}$\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "A23Asms-wz6T"
   },
   "source": [
    "#### Codice\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "vd92w1JFxddU"
   },
   "source": [
    ">\n",
    "    ```\n",
    "    codice\n",
    "    ```\n",
    "    \n",
    "```python\n",
    "import numpy as np\n",
    "a = np.array[1,2,3,4]\n",
    "print(\"Vettore:\", a)\n",
    "s  = np.sum()\n",
    "print(\"Somma degli elementi del vettore: \" + s)\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "MX9wOmgKxyBw"
   },
   "source": [
    "#### Link\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "wQZTXBkZyOzK"
   },
   "source": [
    "> \n",
    "    [Github Repository](https://github.com/visiont3lab/tecnologie_data_science)\n",
    "\n",
    "[Github Repository](https://github.com/visiont3lab/tecnologie_data_science)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "THFS-ugLyKDz"
   },
   "source": [
    "#### Image"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "vr6NPQjGyqj7"
   },
   "source": [
    "> \n",
    "    ![visionlab](https://raw.githubusercontent.com/visiont3lab/tecnologie_data_science/master/book/logo.png)\n",
    "\n",
    "![visionlab](https://raw.githubusercontent.com/visiont3lab/tecnologie_data_science/master/book/logo.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "CdJECheqzBUc"
   },
   "source": [
    "#### Table\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "UJxDXBqZzM19"
   },
   "source": [
    ">\n",
    "\t| Syntax | Description |\n",
    "    | ----------- | ----------- |\n",
    "    | Header | Title |\n",
    "    | Paragraph | Text |\n",
    "\n",
    "| Syntax | Description |\n",
    "| ----------- | ----------- |\n",
    "| Header | Title |\n",
    "| Paragraph | Text |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "GTvSPLUE2k-A"
   },
   "source": [
    "## Code Cell\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Fk2UDy_U36OM"
   },
   "source": [
    "Una cella di codice serve per eseguire un programma scritto in linguaggio python. In particolare utilizzeremo sempre Python 3 Jupyter notebook.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "xKoClhZ337Ft"
   },
   "source": [
    "### Shortcut per eseguire codice\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "NPlE2u6P47eQ"
   },
   "source": [
    "* Cliccando l'icona di **Play** della cella il codice contenuto in essa viene eseguito.\n",
    "* Cliccando  **Cmd/Ctrl+Enter** viene eseguito il codice della cella evidenziata.\n",
    "* Cliccando **Shift+Enter** viene eseguito il codice della cella evidenziata e il focus passa alla cella sucessiva.\n",
    "* Cliccando **Alt+Enter** viene eseguito il codice della cella e viene inserita anche un nuovo blocco di codice.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "HhqD7dvQ5epq"
   },
   "source": [
    "### Suggerimenti e auto-completamento\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "_K6xnHAT5jWM"
   },
   "source": [
    "Google Colab ha un sistema di suggerimenti che viene triggerato schicciando Tab mentre si scrive codice. Esso è utile per scrivere velocemente codice ed evitare errori di sintassi."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "mvgNkjkU33sE"
   },
   "outputs": [],
   "source": [
    "# Importare la libreria numpy\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "jwNLfCxU7HND"
   },
   "outputs": [],
   "source": [
    "np.random. \n",
    "# Se si prova a scrivere np.random. vedremo\n",
    "# apparire dei suggerimenti\n",
    "# Se premiamo Tab apparirà anche la documentazione\n",
    "# associata alla funzione"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "KslBi0ea53P5"
   },
   "outputs": [],
   "source": [
    "np.random?\n",
    "# Premendo Shift-Enter viene aperta la documentazione\n",
    "# Il punto interrogativo alla fine chiama\n",
    "# la documentazione quando il codice viene \n",
    "# eseguito"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "oFEj2Tdc7m0o"
   },
   "source": [
    "### Esecuzione di comandi \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "uuduJjV58f54"
   },
   "source": [
    "Google Colaboratory ci permette mediante uno Jupyter Notebook di eseguire il nostro codice Python e Mardown. Tuttavia è possibile che vi sia la necessità di installare programmi sul computer che ospita il nostro notebook. Per face ciò è necessario create una cella di codice e inserire il punto esclamativo **!** davanti all'istruzione che si vuole eseguire. Quest'ultima sarà un instruzione di sistema.  Siccome la macchina che ospita il nostro notebook è una macchina linux è comune utilizzare il linguaggio **bash** per eseguire comando.\n",
    "\n",
    "Jupyter notebbok fornisce una lista di comandi che possono essere utilizzati per scrivere codice javascript (**%%javascript**), nascondere gli ouput del codice eseguito (**%%capture**) o creare un file utilizzando il notebook (**%%writefile**). Tali comandi vanno inseriti all'inizio del blocco di codice. La lista completa dei comandi si trova [Built-in-Magics Commands](https://ipython.readthedocs.io/en/stable/interactive/magics.html#cell-magics) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 33
    },
    "colab_type": "code",
    "id": "kKxrV6Ba-MX7",
    "outputId": "7c788d8a-06b3-40a1-ae6c-048a026358e8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/content\n"
     ]
    }
   ],
   "source": [
    "# Percorso file a cui sono\n",
    "!pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 33
    },
    "colab_type": "code",
    "id": "nzaid7yf9aY_",
    "outputId": "0ffd722f-9fc0-4a3e-b842-5c081c9c89c3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sample_data\n"
     ]
    }
   ],
   "source": [
    "# Elencare i file contenuti in una cartella\n",
    "!ls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 83
    },
    "colab_type": "code",
    "id": "bU9Od1Oi9a66",
    "outputId": "a2671dce-7b1c-46fe-a543-80cce022f985"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "anscombe.json\t\t      mnist_test.csv\n",
      "california_housing_test.csv   mnist_train_small.csv\n",
      "california_housing_train.csv  README.md\n",
      "/content/sample_data\n"
     ]
    }
   ],
   "source": [
    "# Elencare i file contenuti in una cartella\n",
    "!cd sample_data && ls "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 33
    },
    "colab_type": "code",
    "id": "DUQS50tz9oXb",
    "outputId": "51c3e0d1-fe80-4cf7-f2d7-8bf643022ac3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/content/sample_data\n"
     ]
    }
   ],
   "source": [
    "# Entrare in un cartella\n",
    "%cd sample_data/ "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 33
    },
    "colab_type": "code",
    "id": "3aNomQb-9xxF",
    "outputId": "f3827920-7da3-4a57-a880-a5ffd3644c2e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/content\n"
     ]
    }
   ],
   "source": [
    "# Muovermi nella cartella superiore\n",
    "%cd .."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "f44jon7X-2fR"
   },
   "outputs": [],
   "source": [
    "# Nascondere l'output di un comando.\n",
    "# I comandi di installazione generano output verbosi che si \n",
    "# possono nascondere aggiungendo alla cella il comando\n",
    "%%capture\n",
    "\n",
    "# Installare un pacchetto python usando\n",
    "# pip package manager\n",
    "# !pip list # elencare i pacchetti installati\n",
    "!pip install names \n",
    "!apt-get -qq install -y libfluidsynth1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 133
    },
    "colab_type": "code",
    "id": "sWag_S9cAu_K",
    "outputId": "7121e532-15a7-4365-c253-4d160502d389"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cloning into 'tecnologie_data_science'...\n",
      "remote: Enumerating objects: 41, done.\u001b[K\n",
      "remote: Counting objects: 100% (41/41), done.\u001b[K\n",
      "remote: Compressing objects: 100% (33/33), done.\u001b[K\n",
      "remote: Total 41 (delta 8), reused 12 (delta 0), pack-reused 0\u001b[K\n",
      "Unpacking objects: 100% (41/41), done.\n",
      "sample_data  tecnologie_data_science\n"
     ]
    }
   ],
   "source": [
    "# Clonare un repositorio git \n",
    "!git clone https://github.com/visiont3lab/tecnologie_data_science.git && ls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 33
    },
    "colab_type": "code",
    "id": "XBkfgDSX_BX9",
    "outputId": "568e0e40-4de4-4e17-dd5d-7316132a57cd"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overwriting myLib.py\n"
     ]
    }
   ],
   "source": [
    "# Creazione di un file python myLib.py\n",
    "%%writefile myLib.py\n",
    "import numpy as np\n",
    "import plotly.graph_objects as go\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "def myplot(x,y):\n",
    "    #fig = go.Figure()\n",
    "    #fig.add_trace( go.Scatter(x=x,y=y, mode=\"lines+markers\", name=\"Line\") )\n",
    "    fig = plt.plot(x,y)\n",
    "    return fig\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 264
    },
    "colab_type": "code",
    "id": "HxnRidB4JWdh",
    "outputId": "85177256-2ea4-46ec-b15a-a1a8712d4bac"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Utilizzo del file mylib.py creato\n",
    "from importlib import reload \n",
    "import myLib\n",
    "myLib = reload(myLib)\n",
    "\n",
    "x = np.arange(0.0, 2.0, 0.01)\n",
    "y = 1 + np.sin(2 * np.pi * x)\n",
    "fig = myLib.myplot(x,y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 183
    },
    "colab_type": "code",
    "id": "hkf_xNEwD7lH",
    "outputId": "40afdc4a-6756-426f-d7cc-fa4d34354313"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "m.txt\n",
      "myLib.py\n",
      "name.txt\n",
      "__pycache__\n",
      "sample_data\n",
      "tecnologie_data_science\n",
      "test.py\n",
      "1\n",
      "2\n",
      "3\n"
     ]
    }
   ],
   "source": [
    "%%script bash\n",
    "# È possibile scrivere codice bash\n",
    "ls > name.txt;\n",
    "cat name.txt\n",
    "for i in 1 2 3; do\n",
    "    echo $i\n",
    "done"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "dtAVg2sDKS4O"
   },
   "source": [
    "## Montare Google Drive come hard disk esterno"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "uk-pQLgBLT7O"
   },
   "source": [
    "Una delle qualità di Google Colaboratory  è il fatto che esso è connesso a Google Drive. Questo fa si che i notebook che vengono creati utilizzando Google Colab vengano automaticamente salvati nel drive dell'utente. Inoltre è possibile accedere al prorpio drive e lavorare con dei file contenuti in esso. Questo approccio è estramente interessante in quanto permette di combinare l'abilità di scrivere codice di Google Colab con le capacità di archiviazione file fornite da Google Drive.  Di seguito `è riporatata la sequenza di comandi necessaria per montare il drive dell'utente all'interno di Google Colab. \n",
    "\n",
    "La sequenza di instruzioni sucessiva equivale al bottone chiamata **Mount Drive** che si a sinistra nel gruppo **Files**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 120
    },
    "colab_type": "code",
    "id": "n4LgN1RMKaFv",
    "outputId": "fda156fb-b770-4fa6-a7e9-825a8bb7cdeb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
      "\n",
      "Enter your authorization code:\n",
      "··········\n",
      "Mounted at /content/drive\n"
     ]
    }
   ],
   "source": [
    "from google.colab import drive\n",
    "drive.mount('/content/drive')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 66
    },
    "colab_type": "code",
    "id": "LBpIP587KvUD",
    "outputId": "418997a7-9bfb-4dc3-8fc9-8297b3f68347"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/content/drive/My Drive/lessons/tecnologie_data_science\n",
      "_Dani  Market-Basket-Analysis-master  _template_PySpark\n",
      "_Manu  tecnologie_data_science.xlsx\n"
     ]
    }
   ],
   "source": [
    "%cd /content/drive/My Drive/lessons/tecnologie_data_science\n",
    "!ls"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "google-colab.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}