chapter3_pcli.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "chapter3-pcli.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "nP_6tcHM7yFm"
      ],
      "authorship_tag": "ABX9TyOkpCsPCKl7ZCojg/C7YZ4c",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/paiml/python-command-line-tools/blob/master/chapter3_pcli.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nP_6tcHM7yFm",
        "colab_type": "text"
      },
      "source": [
        "### Using IPython, Jupyter, Colab and Python executable\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n7JKyOrPkITH",
        "colab_type": "text"
      },
      "source": [
        "#### Using IPython\n",
        "\n",
        "Very similar to Jupyter, but run from terminal:\n",
        "\n",
        "*   IPython predates Jupyter\n",
        "*   Both Jupyter and IPython accept *!ls -l* format to execute shell commands\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YR3ZddpWM-uh",
        "colab_type": "code",
        "outputId": "b5d41cf4-1e89-44fd-d46e-aa5b8efbadc1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "!ls -l"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "total 4\n",
            "drwxr-xr-x 1 root root 4096 Nov 21 16:30 sample_data\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RETmEcecyCVf",
        "colab_type": "code",
        "outputId": "c550da6e-d934-4336-aa0e-da279950ab05",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "var = !ls -l\n",
        "type(var)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "IPython.utils.text.SList"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dQMhyAm8sxSc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#var.fields?"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qGSSYqVOyJEP",
        "colab_type": "code",
        "outputId": "6cda1772-4401-4d13-90e8-be5bf1c24951",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "var.grep(\"data\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['drwxr-xr-x 1 root root 4096 Nov 21 16:30 sample_data']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Pci2aotqPIME",
        "colab_type": "text"
      },
      "source": [
        "#### Jupyter Notebook\n",
        "\n",
        "\n",
        "Many flavors of Jupyter Notebook.  A few popular ones:\n",
        "\n",
        "* ![Jupyter](https://user-images.githubusercontent.com/58792/40282633-395be25c-5c27-11e8-9e40-357ea4216562.png)\n",
        "* ![JupyterHug](https://user-images.githubusercontent.com/58792/40282632-387efe32-5c27-11e8-9f02-6f95f2fee223.png)\n",
        "* ![Colab](https://user-images.githubusercontent.com/58792/40282631-384f8cf6-5c27-11e8-9209-3f0d22de0d81.png)\n",
        "* ![Kaggle](https://user-images.githubusercontent.com/58792/40282634-3985c3a6-5c27-11e8-8c53-06fbdedce847.png)\n",
        "* ![Sagemaker](https://user-images.githubusercontent.com/58792/40282635-39a3bdd4-5c27-11e8-81d5-6533a3b84771.png)\n",
        "\n",
        "#### Hosted Commercial Flavors\n",
        "\n",
        "* [Google Colaboratory](https://colab.research.google.com/notebook):  Free\n",
        "* [Kaggle](https://www.kaggle.com/):  Free\n",
        "\n",
        "\n",
        "#### Pure Open Source\n",
        "\n",
        "* [Jupyter](http://jupyter.org/) standalone, original\n",
        "* [JupyterHub](https://github.com/jupyterhub/jupyterhub) multi-user, docker friendly\n",
        "\n",
        "#### Hybrid Solutions\n",
        "\n",
        "* Running Jupyter on [AWS Spot Instances](https://aws.amazon.com/ec2/spot/)\n",
        "* [Google Data Lab](https://cloud.google.com/datalab/)\n",
        "* [Azure Data Science Virtual Machines](https://azure.microsoft.com/en-us/services/virtual-machines/data-science-virtual-machines/)\n",
        "* [AWS Sagemaker](https://aws.amazon.com/sagemaker/)\n",
        "* [Azure Machine Learning Studio](https://studio.azureml.net/)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lh4tsf3-p-lw",
        "colab_type": "text"
      },
      "source": [
        "#### Colab Notebook Key Features\n",
        "\n",
        "* [12.1 Perform Colaboratory basics ](https://www.safaribooksonline.com/videos/essential-machine-learning/9780135261118/9780135261118-EMLA_01_12_01)\n",
        "\n",
        "* [12.2 Use Advanced Colab Features](https://www.safaribooksonline.com/videos/essential-machine-learning/9780135261118/9780135261118-EMLA_01_12_02)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "V-xv1E3YqDK4",
        "colab_type": "text"
      },
      "source": [
        "*  Can enable both TPU and GPU runtimes\n",
        "*  Can upload regular Jupyter Notebooks into colab\n",
        "*  Can have a Google Drive Full of Colab Notebooks\n",
        "*  Can sync colab notebooks to Github.  Here is an example of a [gist of this notebook](https://gist.github.com/noahgift/c69200e05c057cf239fc7ea0be62e043)\n",
        "*  Can connect to a [local runtime](https://research.google.com/colaboratory/local-runtimes.html)\n",
        "* Can create [forms in Colab](https://colab.research.google.com/notebooks/forms.ipynb)\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hPWO_zyRopXN",
        "colab_type": "text"
      },
      "source": [
        "##### Mount GDrive\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XI73HZNLobp4",
        "colab_type": "code",
        "outputId": "f9810fa8-fc58-40ac-e9c3-61d47e6b1dd6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 124
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive', force_remount=True)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "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&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&response_type=code\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/gdrive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UNyzZwgmoxwm",
        "colab_type": "code",
        "outputId": "7a0d2711-c2ad-457e-ae40-86ec8b7dc706",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "import os;os.listdir(\"/content/gdrive/My Drive/awsml\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['kaggle.json', 'credentials', 'config', 'cloudai-7ab42a4d8a43.json']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3jX9349SpLbr",
        "colab_type": "text"
      },
      "source": [
        "#### Magic Commands"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Zf35CYV9PrjS",
        "colab_type": "text"
      },
      "source": [
        "##### %timeit"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tMpMJH0rfAEg",
        "colab_type": "code",
        "outputId": "07047e16-3143-4cfb-dfcc-6993b53ddc54",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 89
        }
      },
      "source": [
        "too_many_decimals = 1.912345897\n",
        "\n",
        "print(\"built in Python Round\")\n",
        "%timeit round(too_many_decimals, 2)\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "built in Python Round\n",
            "The slowest run took 28.69 times longer than the fastest. This could mean that an intermediate result is being cached.\n",
            "1000000 loops, best of 3: 468 ns per loop\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p_KLutZKq5yN",
        "colab_type": "text"
      },
      "source": [
        "##### %alias"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jyIZLH6UpMou",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "alias lscsv ls -l sample_data/*.csv "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BNJ6bKtGpVn3",
        "colab_type": "code",
        "outputId": "2307a6a0-dbb4-4d4f-e622-6aebf457c46c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 87
        }
      },
      "source": [
        "lscsv"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "-rw-r--r-- 1 root root   301141 Oct 25 16:58 sample_data/california_housing_test.csv\n",
            "-rw-r--r-- 1 root root  1706430 Oct 25 16:58 sample_data/california_housing_train.csv\n",
            "-rw-r--r-- 1 root root 18289443 Oct 25 16:58 sample_data/mnist_test.csv\n",
            "-rw-r--r-- 1 root root 36523880 Oct 25 16:58 sample_data/mnist_train_small.csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AE1cfp41rcwL",
        "colab_type": "text"
      },
      "source": [
        "[Reference These]\n",
        "https://ipython.readthedocs.io/en/stable/interactive/magics.html"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MnsQIiDurIPp",
        "colab_type": "text"
      },
      "source": [
        "##### %who"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ljy17rdjrWD9",
        "colab_type": "text"
      },
      "source": [
        "Print variables"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mUFS07KpPfE6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "var1=1"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ACyoq3IsrNZV",
        "colab_type": "code",
        "outputId": "a4d09d85-6295-46b9-ed74-c74e156a45ae",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "who"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "drive\t os\t too_many_decimals\t var\t var1\t \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MURtyzAMrZ1Z",
        "colab_type": "code",
        "outputId": "25224048-22c8-4df3-b579-924a09f674d6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "too_many_decimals"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.912345897"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l9z-cAQow-BO",
        "colab_type": "text"
      },
      "source": [
        "##### %writefile"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "luIP2qKqxJmB",
        "colab_type": "code",
        "outputId": "33d40f6c-f110-4d99-ad95-e422ea1c7fe4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "%%writefile magic_stuff.py\n",
        "import pandas as pd\n",
        "df = pd.read_csv(\n",
        "    \"https://raw.githubusercontent.com/noahgift/food/master/data/features.en.openfoodfacts.org.products.csv\")\n",
        "df.drop([\"Unnamed: 0\", \"exceeded\", \"g_sum\", \"energy_100g\"], axis=1, inplace=True) #drop two rows we don't need\n",
        "df = df.drop(df.index[[1,11877]]) #drop outlier\n",
        "df.rename(index=str, columns={\"reconstructed_energy\": \"energy_100g\"}, inplace=True)\n",
        "print(df.head())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Writing magic_stuff.py\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uok6ppvvxRo9",
        "colab_type": "code",
        "outputId": "13ef744b-8830-4be8-bda9-1fd4dcf6dc47",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 159
        }
      },
      "source": [
        "cat magic_stuff.py"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "import pandas as pd\n",
            "df = pd.read_csv(\n",
            "    \"https://raw.githubusercontent.com/noahgift/food/master/data/features.en.openfoodfacts.org.products.csv\")\n",
            "df.drop([\"Unnamed: 0\", \"exceeded\", \"g_sum\", \"energy_100g\"], axis=1, inplace=True) #drop two rows we don't need\n",
            "df = df.drop(df.index[[1,11877]]) #drop outlier\n",
            "df.rename(index=str, columns={\"reconstructed_energy\": \"energy_100g\"}, inplace=True)\n",
            "print(df.head())"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "STjEo5XFPxM0",
        "colab_type": "code",
        "outputId": "702f080e-e89a-4443-ddeb-af0109a7fcaf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 157
        }
      },
      "source": [
        "!python magic_stuff.py"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "   fat_100g  ...                         product\n",
            "0     28.57  ...  Banana Chips Sweetened (Whole)\n",
            "2     57.14  ...          Organic Salted Nut Mix\n",
            "3     18.75  ...                  Organic Muesli\n",
            "4     36.67  ...                   Zen Party Mix\n",
            "5     18.18  ...            Cinnamon Nut Granola\n",
            "\n",
            "[5 rows x 7 columns]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0jUCwijQ0_-t",
        "colab_type": "code",
        "outputId": "1afead35-1416-446f-97ee-b444aded21e1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104
        }
      },
      "source": [
        "!pip install -q pylint"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[K     |████████████████████████████████| 307kB 3.5MB/s \n",
            "\u001b[K     |████████████████████████████████| 51kB 5.6MB/s \n",
            "\u001b[K     |████████████████████████████████| 215kB 46.1MB/s \n",
            "\u001b[K     |████████████████████████████████| 737kB 41.7MB/s \n",
            "\u001b[K     |████████████████████████████████| 61kB 7.4MB/s \n",
            "\u001b[?25h"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xf8MwJfy1FqQ",
        "colab_type": "code",
        "outputId": "d3c20211-3352-4109-b77a-4dfecb6a2a6d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 262
        }
      },
      "source": [
        "!pylint magic_stuff.py"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "************* Module magic_stuff\n",
            "magic_stuff.py:3:0: C0301: Line too long (109/100) (line-too-long)\n",
            "magic_stuff.py:4:0: C0301: Line too long (110/100) (line-too-long)\n",
            "magic_stuff.py:5:24: C0326: Exactly one space required after comma\n",
            "df = df.drop(df.index[[1,11877]]) #drop outlier\n",
            "                        ^ (bad-whitespace)\n",
            "magic_stuff.py:7:0: C0304: Final newline missing (missing-final-newline)\n",
            "magic_stuff.py:1:0: C0114: Missing module docstring (missing-module-docstring)\n",
            "magic_stuff.py:2:0: C0103: Constant name \"df\" doesn't conform to UPPER_CASE naming style (invalid-name)\n",
            "magic_stuff.py:5:0: C0103: Constant name \"df\" doesn't conform to UPPER_CASE naming style (invalid-name)\n",
            "\n",
            "------------------------------------\n",
            "Your code has been rated at -1.67/10\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TtVDMB8EwJqG",
        "colab_type": "text"
      },
      "source": [
        "##### Bash"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YC03T7XUuF1k",
        "colab_type": "code",
        "outputId": "02522270-1f36-47aa-bb5f-f6fce6d2e5aa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 367
        }
      },
      "source": [
        "%%bash\n",
        "uname -a\n",
        "ls\n",
        "ps"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Linux bee9b4559c13 4.14.137+ #1 SMP Thu Aug 8 02:47:02 PDT 2019 x86_64 x86_64 x86_64 GNU/Linux\n",
            "gdrive\n",
            "magic_stuff.py\n",
            "sample_data\n",
            "    PID TTY          TIME CMD\n",
            "      1 ?        00:00:00 run.sh\n",
            "      9 ?        00:00:01 node\n",
            "     24 ?        00:00:03 jupyter-noteboo\n",
            "    119 ?        00:00:00 tail\n",
            "    127 ?        00:00:06 python3\n",
            "    298 ?        00:00:00 bash\n",
            "    299 ?        00:00:00 drive\n",
            "    300 ?        00:00:00 grep\n",
            "    383 ?        00:00:01 drive\n",
            "    394 ?        00:00:00 fusermount <defunct>\n",
            "    450 ?        00:00:00 bash\n",
            "    451 ?        00:00:00 tail\n",
            "    452 ?        00:00:00 grep\n",
            "    551 ?        00:00:00 bash\n",
            "    554 ?        00:00:00 ps\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HI8JuclMt0p2",
        "colab_type": "code",
        "outputId": "d8fcd487-28d4-4c83-bf10-394a66322620",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "!uname -a"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Linux bee9b4559c13 4.14.137+ #1 SMP Thu Aug 8 02:47:02 PDT 2019 x86_64 x86_64 x86_64 GNU/Linux\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ohA_HAOewNMT",
        "colab_type": "text"
      },
      "source": [
        "##### Python2"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eioZW4txwO7-",
        "colab_type": "code",
        "outputId": "bb8af4ac-d6b6-4313-f27e-1294dc1b3b39",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "%%python2\n",
        "print \"old school\"\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "old school\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kn-P-e_hjvK5",
        "colab_type": "text"
      },
      "source": [
        "#### Python executable\n",
        "\n",
        "Can run scripts, REPL and even run python statements with -c flag and semicolon to string together multiple statements"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p2aDegQxjegv",
        "colab_type": "code",
        "outputId": "417a8e03-80a3-43bd-e272-c502f294523f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "!python -c \"import os;print(os.listdir())\""
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['.config', 'magic_stuff.py', 'gdrive', 'sample_data']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WaXhDeUM_49R",
        "colab_type": "code",
        "outputId": "09858dd1-ae6b-40e5-8bfc-538ad926fb87",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 103
        }
      },
      "source": [
        "!ls -l"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "total 712\n",
            "drwx------ 4 root root   4096 Sep  9 16:16 gdrive\n",
            "-rw-r--r-- 1 root root    407 Sep  9 16:20 magic_stuff.py\n",
            "-rw-r--r-- 1 root root 712814 Sep  9 16:25 pytorch.pptx\n",
            "drwxr-xr-x 1 root root   4096 Aug 27 16:17 sample_data\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "58zCAVUC_8JP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!pip install -q yellowbrick"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "egHxTJUGNYCA",
        "colab_type": "code",
        "outputId": "fa298362-f0fa-4fa2-cfd1-0b6a02da5bb6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        }
      },
      "source": [
        "#this is how you capture input to a program\n",
        "import sys;sys.argv"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py',\n",
              " '-f',\n",
              " '/root/.local/share/jupyter/runtime/kernel-559953d0-ac45-4a1a-a716-8951070eaab5.json']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    }
  ]
}