01_mini_tour_colab.ipynb

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  "metadata": {
    "colab": {
      "name": "01_mini-tour-colab.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "authorship_tag": "ABX9TyNR3PWSkkpzM155a3YMncKn",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/lindajune/handson-2021-code-testing/blob/main/01_mini_tour_colab.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NqDKvwtyjowR"
      },
      "source": [
        "#Practice Python With Google Colab\n",
        "\n",
        "---\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q3R6Jv4zJEyQ"
      },
      "source": [
        "This mini tour of Google Colab was adapted from the Colab Tutorial by [Kevin Zakka](https://cs231n.github.io/) and an introduction of Colab by [Shuyi Wang](https://towardsdatascience.com/how-to-practice-python-with-google-colab-45fc6b7d118b). It runs Python3 by default."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Sm0Zt_1ulPwq"
      },
      "source": [
        "## Introduction\n",
        "For python beginners, they could run into the following practical problems easily:\n",
        "\n",
        "    Do not know how to install and set up the Python running environment;\n",
        "    Do not know how to find the solutions effectively when facing the problems;\n",
        "    Do not know how to collaborate with others when trying to finish the group tasks；\n",
        "    Do not know how to handle version control, which may lead the code to chaotic.\n",
        "\n",
        "Google Colab is a suitable tool for beginners to practice Python. It supports automatic setting-up, getting help effectively, collaborative programming, and version control.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7q2S1WpzohJo"
      },
      "source": [
        "## How to use it?\n",
        "Just open a browser（I recommend Google Chrome or Firefox), and input the following URL: https://colab.research.google.com\n",
        "\n",
        "Now creat your code and run it!\n",
        "\n",
        "Watch [Introduction to Colab](https://www.youtube.com/watch?v=inN8seMm7UI) to learn more, or just get started below!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J9R_QbEbo5kC"
      },
      "source": [
        "## Python Version\n",
        "\n",
        "As of Janurary 1, 2020, Python has [officially dropped support](https://www.python.org/doc/sunset-python-2/) for `python2`. You can check your Python version at the command line by running `python --version`. Note that as of June 2021, Colab uses Python 3.7.10 which should run everything without any errors."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pFnQnYEFpaWW"
      },
      "source": [
        "!python --version"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T30SpoW1qiMC"
      },
      "source": [
        "## Packages or Libraries\n",
        "Although you did not go through any installation processes, it still contains almost all the modules you need for data science analysis.\n",
        "\n",
        "These tools include but are not limited to Numpy, Scipy, Pandas, etc. Even deep learning frameworks, such as Tensorflow, Keras and Pytorch are also included."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qSS0rC-trngE"
      },
      "source": [
        "### 1. version check"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y43klDOFrgEG",
        "outputId": "988c2f2c-5602-4b35-bd01-583ae512283f"
      },
      "source": [
        "!pip list -v | grep numpy"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "numpy                         1.19.5              /usr/local/lib/python3.7/dist-packages pip      \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "inqgxypLrxUZ",
        "outputId": "e971ed22-150e-453f-f0e9-06ed4886d15b"
      },
      "source": [
        "!pip list -v | grep pandas"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "pandas                        1.1.5               /usr/local/lib/python3.7/dist-packages pip      \n",
            "pandas-datareader             0.9.0               /usr/local/lib/python3.7/dist-packages pip      \n",
            "pandas-gbq                    0.13.3              /usr/local/lib/python3.7/dist-packages pip      \n",
            "pandas-profiling              1.4.1               /usr/local/lib/python3.7/dist-packages pip      \n",
            "sklearn-pandas                1.8.0               /usr/local/lib/python3.7/dist-packages pip      \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Un4aucxgrzlE"
      },
      "source": [
        "### 2. import a library that s in Colab"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-eTNPvR0q9HW"
      },
      "source": [
        "import numpy as np"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e_hgTMNjrAl3"
      },
      "source": [
        "import pandas as pd"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i9aJP_fHrD1Q"
      },
      "source": [
        "import scipy"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2zP1kkvdrK2B"
      },
      "source": [
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p9JyRy6RsADv"
      },
      "source": [
        "### 3. importing a library that is not in Colab\n",
        "\n",
        "To import a library that's not in Colab by default, you can use !pip install or !apt-get install."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vDWhhgZDsomO"
      },
      "source": [
        "!pip install matplotlib-venn"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nAlln7y9tw_f"
      },
      "source": [
        "# https://pypi.python.org/pypi/pydot\n",
        "!apt-get -qq install -y graphviz && pip install pydot\n",
        "import pydot"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jFbeE1ZTuG-X"
      },
      "source": [
        "### 4. python function\n",
        "Define a python function using the `def` keyword. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "21i7Y062ujbq"
      },
      "source": [
        "def sign(x):\n",
        "    if x > 0:\n",
        "        return 'positive'\n",
        "    elif x < 0:\n",
        "        return 'negative'\n",
        "    else:\n",
        "        return 'zero'\n",
        "\n",
        "for x in [-1, 0, 1]:\n",
        "    print(sign(x))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "62ivyYMduq7l"
      },
      "source": [
        "Define a function to take optional keyword arguments, for example:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6x097JqLuvkY"
      },
      "source": [
        "def hello(name, loud=False):\n",
        "    if loud:\n",
        "        print('HELLO, {}'.format(name.upper()))\n",
        "    else:\n",
        "        print('Hello, {}!'.format(name))\n",
        "\n",
        "hello('Bob')\n",
        "hello('Fred', loud=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xO2PQvRCoQDX"
      },
      "source": [
        "### 4. plot a heatmap\n",
        "let's try to use `seaborn`"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 420
        },
        "id": "VlgRkSdLo8of",
        "outputId": "c54a4a39-5a8c-4773-8e38-27fb1d29b414"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "# sns.set()\n",
        "\n",
        "# Build data\n",
        "np.random.seed(0) # seed the random number generator in order to make the run repeatable\n",
        "dat = 100*np.random.rand(3, 4)\n",
        "df_labels = pd.DataFrame(dat)\n",
        "\n",
        "# Iterate over numpy array and prepare text labels\n",
        "for (row, col), val in np.ndenumerate(dat):\n",
        "    df_labels.iloc[row, col] = \"%.2f%%\" % (val)\n",
        "    \n",
        "print(df_labels)\n",
        "print('\\n')\n",
        "\n",
        "# plot the heatmap\n",
        "plt.figure()\n",
        "plt.suptitle(\"Seaborn heatmap example to display 2d percentage values\", color='m')\n",
        "sns.set(font_scale=0.8)\n",
        "\n",
        "ax = sns.heatmap(dat, linewidths=.5, annot=df_labels, fmt=\"\", annot_kws={\"size\": 12}, vmin=0, vmax=100)\n",
        "ax.set(xlabel='x label', ylabel='y label')\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "        0       1       2       3\n",
            "0  54.88%  71.52%  60.28%  54.49%\n",
            "1  42.37%  64.59%  43.76%  89.18%\n",
            "2  96.37%  38.34%  79.17%  52.89%\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N25tVLE3vcq9"
      },
      "source": [
        "### 5. advanced examples\n",
        "More examples to practice beyond this hands-on session could be found: [Colab Tutorials for Python Programming](https://colab.research.google.com/github/cs231n/cs231n.github.io/blob/master/python-colab.ipynb#scrollTo=dzNng6vCL9eP). "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pjQ_MZVYwFKQ"
      },
      "source": [
        ""
      ]
    }
  ]
}