Demo20200307.ipynb

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  "metadata": {
    "colab": {
      "name": "Demo20200307.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EfHBgIiLzu1-",
        "colab_type": "text"
      },
      "source": [
        "## Python Basic"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wVlE92ajy_Hy",
        "colab_type": "code",
        "outputId": "cb3a8f71-43cf-4e2b-9e2b-fbfc0058770f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "a = 3\n",
        "b = 2\n",
        "a + b"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xfLeTSi00qkt",
        "colab_type": "code",
        "outputId": "e2cde34b-6e71-4433-8f06-42b013b182c2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 306
        }
      },
      "source": [
        "! nvidia-smi"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Sat Mar  7 02:28:53 2020       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 440.59       Driver Version: 418.67       CUDA Version: 10.1     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla K80           Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   70C    P8    32W / 149W |      0MiB / 11441MiB |      0%      Default |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                       GPU Memory |\n",
            "|  GPU       PID   Type   Process name                             Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Guwkf4HN88y9",
        "colab_type": "text"
      },
      "source": [
        "## Number"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BJ5lhGH50uT6",
        "colab_type": "code",
        "outputId": "f549f7c8-7b1d-4010-a56e-25179a91a579",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "3 + 2 * 8"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "19"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9kfrH4To9Fjc",
        "colab_type": "code",
        "outputId": "8d275074-6403-412f-d118-0b851208ea84",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "2 + 1"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ONEi6a4C9LEl",
        "colab_type": "code",
        "outputId": "b9528de6-c51d-495e-9927-c5bbfbb90105",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "2 - 1"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n5INZlxv9M8o",
        "colab_type": "code",
        "outputId": "7f7d42bb-ce6d-48d4-9061-13aa89af5426",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "2 * 3"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "6"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FsWBJAQF9Nk0",
        "colab_type": "code",
        "outputId": "b7f4b3b4-c9bf-4fdd-8b65-397d4858a989",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "3 / 2"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.5"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bNQhRv4l9OOc",
        "colab_type": "code",
        "outputId": "aa33d5ae-74dc-4e86-f1e4-23403c7c639d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "2 ** 3"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "8"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "igqFmLzH9YQO",
        "colab_type": "code",
        "outputId": "b1baa942-8ec4-473b-c9bf-32f13bca5507",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "4 ** 0.5"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3nOtVpys9aln",
        "colab_type": "code",
        "outputId": "185676e8-5ec8-4f9c-f20d-0b58caa1a7ed",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "2 + 10 * 10 + 3"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "105"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aaHWovSC9d57",
        "colab_type": "code",
        "outputId": "b421bb57-b367-4cee-e751-4637ff08fc32",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "(2 + 10) * (10 + 3)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "156"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O7vQvYmJ9jsp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "a = 5"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4mzV9WJP9pM6",
        "colab_type": "code",
        "outputId": "d0dab7c6-298b-49e3-a6d5-617d18ad90c8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "a + a"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "10"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ee0OjpmV9t47",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "a = 10"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qB6O2xBa9wVx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "a = a + a"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uIy1pkEU91Ey",
        "colab_type": "code",
        "outputId": "3ac9759f-379a-499f-e530-4d708a9d0197",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "a"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "20"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cQI0dDAp91lk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "price = 49.99\n",
        "exchange_rate = 32.33"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "otFXOp-D-Ddz",
        "colab_type": "code",
        "outputId": "768fd05c-7034-43f4-cbe4-f4aa883b2b6e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "price * exchange_rate"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1616.1767"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SRYkJ-Ui-zkp",
        "colab_type": "code",
        "outputId": "b5c2b8aa-60db-4c36-e319-b0c71aebb62f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 166
        }
      },
      "source": [
        "PRICE"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-18-d3b79827e3c0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mPRICE\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m: name 'PRICE' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-42JQtjM-P9a",
        "colab_type": "text"
      },
      "source": [
        "**執行當下的cell**\n",
        "\n",
        "> ctrl + enter\n",
        "\n",
        "\n",
        "\n",
        "**執行當下的cell, 新增一個cell**\n",
        "\n",
        "\n",
        "> shift + enter\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kJ5imdHa_dT-",
        "colab_type": "text"
      },
      "source": [
        "## String"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zpNXsYnF-Lob",
        "colab_type": "code",
        "outputId": "45a9e9bf-8ee7-46be-c76d-f546f4989d9c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "'this is a string'"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'this is a string'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wvS-bmEF_iwd",
        "colab_type": "code",
        "outputId": "7de4d02c-7aed-4f80-9df1-2182a88f3c3d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "\"this is a string\""
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'this is a string'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NqGInrO8_kze",
        "colab_type": "code",
        "outputId": "7d81be5b-aa90-46f6-b585-16b6e1284e55",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "'hello world'"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'hello world'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rA0zY3A_AFrb",
        "colab_type": "code",
        "outputId": "29dff372-8b30-4e9d-aa41-953080434572",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "'hello'\n",
        "'world'"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'world'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1tV3gkMWAIBy",
        "colab_type": "code",
        "outputId": "98ae28e5-9722-4105-9b87-f79e9b2b383a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "print('hello')\n",
        "print('world')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "hello\n",
            "world\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wiABBipcATbF",
        "colab_type": "code",
        "outputId": "7495fcfd-8e04-4446-aa91-12efde80cc5c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "'大家好'"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'大家好'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vMLYpI3BAYRQ",
        "colab_type": "code",
        "outputId": "83db630c-bca9-4aa0-f1d1-fa80448b75b0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 139
        }
      },
      "source": [
        "print('Hello world 1')\n",
        "print('Hello world 2')\n",
        "print('Use \\n to print a new line')\n",
        "print('\\n')\n",
        "print('See what i mean?')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Hello world 1\n",
            "Hello world 2\n",
            "Use \n",
            " to print a new line\n",
            "\n",
            "\n",
            "See what i mean?\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0BGudXNmAfqY",
        "colab_type": "code",
        "outputId": "302ee52c-039e-4aa0-de73-2f79e8dc18eb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "print('this is a line\\nthis is the second line')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "this is a line\n",
            "this is the second line\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GIp0RX8nAxBT",
        "colab_type": "code",
        "outputId": "181d6e3f-8799-4879-bd1e-ff6a78033c67",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "print('1 2 3\\n100 200 300')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1 2 3\n",
            "100 200 300\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "szWkbNIEA2_X",
        "colab_type": "code",
        "outputId": "97a49592-8fe5-477c-9382-040d0e162f67",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "print('1\\t2\\t3\\n100\\t200\\t300')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1\t2\t3\n",
            "100\t200\t300\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2AgVWTLGA9ia",
        "colab_type": "code",
        "outputId": "5e8e5534-e3da-47ed-cc8b-a19cc0eae68d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 55
        }
      },
      "source": [
        "a = 'this is a loooooooooooooooooooooooooooooooooooooooooooooo\\\n",
        "oooooooooooooooooooooooooooooooooooooooooooooooooooooooooong text'\n",
        "print(a)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "this is a loooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooong text\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nPGroEhbBPJQ",
        "colab_type": "code",
        "outputId": "2e188e2b-a99e-434f-a75d-709a64daa196",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104
        }
      },
      "source": [
        "a = '''\n",
        "this is a line\n",
        "this is the second line\n",
        "this is the third line\n",
        "'''\n",
        "print(a)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "this is a line\n",
            "this is the second line\n",
            "this is the third line\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ikixv-hwBX3r",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "s = 'Hello'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YQfPfdpiBw4d",
        "colab_type": "code",
        "outputId": "792faf5b-bb89-440a-f11b-7dc2f4d390ee",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "s[len(s)-1]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'o'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XBXvepHQB0Vs",
        "colab_type": "code",
        "outputId": "3a383213-92dd-43cf-e24a-61ea13ea92d7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "s[0]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'H'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Yw_zyFw_B_nE",
        "colab_type": "code",
        "outputId": "2df3e1aa-5c2c-44ad-d1d4-b768de0d52c6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "s[1]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'e'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p7TvvjfHCBwR",
        "colab_type": "code",
        "outputId": "494db57c-7ac1-4085-b83b-40245f275761",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "s[-1]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'o'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2QmBQ7HSCGl-",
        "colab_type": "code",
        "outputId": "d4998128-5864-4629-ad63-bb591dd0dd98",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "s[-2]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'l'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H8-JlKGdCIkj",
        "colab_type": "code",
        "outputId": "e41d1263-b6de-4a45-c6c4-f4340f3fa26e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "s"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Hello'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6z81LuKjCT7N",
        "colab_type": "code",
        "outputId": "6fb4d8fa-952b-4d59-9273-ce65b3f79722",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "# s[開頭:結尾]\n",
        "s[:]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Hello'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qKeHq5duCahl",
        "colab_type": "code",
        "outputId": "37b268d0-29b3-4d01-cb3d-d063cc42a455",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "s[1:3]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'el'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zSfAs2yBCdXa",
        "colab_type": "code",
        "outputId": "9365095b-927e-4b0e-e0aa-56cbe68822d9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "# for(i=0; i < 3; i++)\n",
        "s[:3]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Hel'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MqB6Upn2CtkB",
        "colab_type": "code",
        "outputId": "7cb92e6b-a388-46d3-9c13-61aa2dac3aa1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "#s[開頭:結尾:每次移動幾步]\n",
        "s[::]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Hello'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r72bTKRJC8oh",
        "colab_type": "code",
        "outputId": "1320238e-28a4-4a9c-e17f-8671794861e5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "s[::1]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Hello'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4d0YNUuBDE-w",
        "colab_type": "code",
        "outputId": "c9b8f809-c468-42de-d80a-d254908800af",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "s[::2]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Hlo'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zOxGOPa7DGyi",
        "colab_type": "code",
        "outputId": "dd7003f6-4412-4f70-db9e-dd02c9a5372a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "s[0:3:2]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Hl'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OoAu37TvDL8Q",
        "colab_type": "code",
        "outputId": "a9049423-3206-4d51-e4be-d29014628084",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "s[::-1]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'olleH'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rbi9o8GmDSdj",
        "colab_type": "code",
        "outputId": "3ed3d60f-0604-4c80-f4d1-c5c1a9136046",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "a = '上海自來水來自海上'\n",
        "a[::-1]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'上海自來水來自海上'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tXvQJE78Dc3b",
        "colab_type": "code",
        "outputId": "ed9dea35-20df-4904-8635-cb790ff5a989",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "a[::-1] == a"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HtaD-GnRDhoq",
        "colab_type": "code",
        "outputId": "c15ee0fc-3547-4554-e900-d7aa988058a8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 166
        }
      },
      "source": [
        "s[0] = 'X'"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-56-68d9ab99b09c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0ms\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'X'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m: 'str' object does not support item assignment"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n-18CpvUDocO",
        "colab_type": "code",
        "outputId": "bdba71f1-e99f-43ff-9312-4c00da87a6d1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "s = 'Hello'\n",
        "s = s + 'World'\n",
        "s"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'HelloWorld'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g9j6z_ExDzAj",
        "colab_type": "code",
        "outputId": "ec4c053a-1dd7-4643-c56d-35518d4177f0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "letter = '?'\n",
        "letter * 10"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'??????????'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uUPKbvFJD5Tm",
        "colab_type": "code",
        "outputId": "1a775c8d-8ece-4c0d-9557-3ddea6a7f0b4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 55
        }
      },
      "source": [
        "s = '對不起'\n",
        "s * 100"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'對不起對不起對不起對不起對不起對不起對不起對不起對不起對不起對不起對不起對不起對不起對不起對不起對不起對不起對不起對不起對不起對不起對不起對不起對不起對不起對不起對不起對不起對不起對不起對不起對不起對不起對不起對不起對不起對不起對不起對不起對不起對不起對不起對不起對不起對不起對不起對不起對不起對不起對不起對不起對不起對不起對不起對不起對不起對不起對不起對不起對不起對不起對不起對不起對不起對不起對不起對不起對不起對不起對不起對不起對不起對不起對不起對不起對不起對不起對不起對不起對不起對不起對不起對不起對不起對不起對不起對不起對不起對不起對不起對不起對不起對不起對不起對不起對不起對不起對不起對不起'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m8MCNkgzD_3G",
        "colab_type": "code",
        "outputId": "9ab9d973-8cd1-4520-8907-e73e469d5e15",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 55
        }
      },
      "source": [
        "print(dir(s))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['__add__', '__class__', '__contains__', '__delattr__', '__dir__', '__doc__', '__eq__', '__format__', '__ge__', '__getattribute__', '__getitem__', '__getnewargs__', '__gt__', '__hash__', '__init__', '__init_subclass__', '__iter__', '__le__', '__len__', '__lt__', '__mod__', '__mul__', '__ne__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__rmod__', '__rmul__', '__setattr__', '__sizeof__', '__str__', '__subclasshook__', 'capitalize', 'casefold', 'center', 'count', 'encode', 'endswith', 'expandtabs', 'find', 'format', 'format_map', 'index', 'isalnum', 'isalpha', 'isdecimal', 'isdigit', 'isidentifier', 'islower', 'isnumeric', 'isprintable', 'isspace', 'istitle', 'isupper', 'join', 'ljust', 'lower', 'lstrip', 'maketrans', 'partition', 'replace', 'rfind', 'rindex', 'rjust', 'rpartition', 'rsplit', 'rstrip', 'split', 'splitlines', 'startswith', 'strip', 'swapcase', 'title', 'translate', 'upper', 'zfill']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ytv261KoEGDC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "s = 'hello'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UZ_NKy2oEM9y",
        "colab_type": "code",
        "outputId": "1a17473f-cefd-4c6b-c98a-abf62867c785",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "s.upper()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'HELLO'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mjsjB66DERp5",
        "colab_type": "code",
        "outputId": "46a02e7b-cc7d-41e5-efb0-a95bb9f2c50f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "s.lower()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'hello'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "31lALIY_EXO2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "?s.upper"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6kGmR7beEjeR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "s = 'hello world'\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eqs3fi0nEpKu",
        "colab_type": "code",
        "outputId": "e09c80f3-e8db-4894-ceb3-dde58091fd24",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "s.split()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['hello', 'world']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6XpIs8xLEq6S",
        "colab_type": "code",
        "outputId": "b2cb2e7b-dc2f-4893-f18b-e154cab073c0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "len(s)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "11"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zfKEjuMyEtL3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "speech = '''\n",
        "大會主席蘇嘉全院長、現場各位貴賓、電視機前和網路上的全體國人同胞，大家早安，大家好。\n",
        "\n",
        "今天，是中華民國一百零八年的國慶日，謝謝來自世界各地的好朋友，跟我們共度這個值得紀念的日子。\n",
        "\n",
        "去年，也是在國慶日，我和大家說，台灣正處在變局當中。世界貿易局勢的變動、國際政治情勢的變化，都讓未來充滿挑戰。我們必須「求穩、應變、進步」，厚植實力、壯大台灣。\n",
        "\n",
        "轉眼又過了一年，世界依然快速變化，甚至更加劇烈。美中貿易戰的持續進行；距離我們不遠的香港，因為「一國兩制」的失敗，正處於失序邊緣。\n",
        "\n",
        "儘管如此，中國依然以「一國兩制台灣方案」，不斷威脅我們，並採取各種文攻武嚇，強烈挑戰了區域的穩定及和平。\n",
        "\n",
        "各位國人同胞，當自由民主受到挑戰，當中華民國的生存發展受到脅迫，我們就必須站出來捍衛。拒絕「一國兩制」，是兩千三百萬台灣人民不分黨派、不分立場，彼此間最大的共識。\n",
        "\n",
        "中華民國已經在台灣屹立超過七十年，一旦接受「一國兩制」，中華民國就沒有生存的空間。身為總統，站出來守護國家主權，不是挑釁，而是我最基本的責任。\n",
        "\n",
        "七十年來，我們共同經歷過種種嚴峻的挑戰。每一次挑戰，不但沒有擊倒我們，反而讓我們更強壯、更堅定。\n",
        "\n",
        "我們一起走過八二三砲戰，一起度過九六年台海危機。一次又一次文攻武嚇，從來就沒有讓台灣人民屈服。我們共同捍衛住腳下這塊土地，也捍衛住國家的主權。\n",
        "\n",
        "我們經歷過退出聯合國的恐慌，也承受過一次又一次的斷交壓力。但台灣人民想要走向世界的決心，不曾有任何改變。\n",
        "\n",
        "一九七零年代的石油危機，一九九七年的亞洲金融風暴，千禧年的網路泡沫，還有十年前的金融海嘯。經濟總是充滿挑戰，但一卡皮箱走遍天下的台商，加上勤奮又有創意的台灣人民，讓我們一次又一次，把危機化做轉機，讓台灣的經濟持續往前走。\n",
        "\n",
        "我們也經歷過八七水災、九二一地震、SARS風暴、八八風災。天災病變的磨難，沒有擊敗過台灣人民奮鬥生存的意志。家園毀壞了，我們重建；土地受傷了，我們復原。擦乾眼淚，重新站起，明天又是充滿希望的一天。\n",
        "\n",
        "這些共同的記憶，彰顯了台灣人民的韌性。因為有這樣的韌性，我們成為亞洲四小龍的一員。因為這樣的韌性，我們走過了民主化的艱鉅歷程，成為世界上重要的民主典範。\n",
        "\n",
        "我們共同走過這一段路，無論是哪個黨派，只要是生活在這塊土地上的人民，都不能分割彼此。中華民國不是誰的專利，台灣也不是誰能夠獨佔。「中華民國台灣」六個字，既不是藍色、也不是綠色，這就是整個社會最大的共識。\n",
        "\n",
        "展望未來，前方的挑戰還有很多，等著我們一一克服。\n",
        "\n",
        "我們看見中國崛起跟擴張，以威權體制，結合民族主義和經濟力量，挑戰自由民主的價值和世界秩序。也因此，處於印太地區戰略前緣的台灣，成為了守護民主價值的第一道防線。\n",
        "\n",
        "中國利用「銳實力」步步進逼，但我們很清楚，作為區域的重要成員，台灣要善盡國際責任。我們不挑釁、不冒進，結合理念相近的國家，確保台海和平穩定的現狀，不會被片面改變。\n",
        "\n",
        "要做到這些，我們必須要團結。儘管台灣社會曾經因為族群、世代、信仰、黨派的差異，而出現爭執，但透過對話，我們一定可以找到彼此可以接受的最大公約數。經驗證明，衝突、對話、團結進步，會引導國家往正確的方向前進。\n",
        "\n",
        "我們也必須堅守自由民主的價值。台灣人民曾經一起走過民主化的艱辛道路，民主偶有紛擾，但只有民主制度，能夠保障得來不易的自由，也才能讓下一代保有決定未來的權利。\n",
        "\n",
        "我們也必須持續壯大台灣。三年多來，我們努力調整經濟結構，引導產業升級轉型、推動國際多元布局。我們迎來投資大爆發，在全球經濟的變動中，站穩了腳步，方向是對的，我們就要繼續走。\n",
        "\n",
        "三年多來，我們努力維護社會公平，加薪減稅、全面照顧，讓全民分享經濟成長的果實。未來，推動長照2.0升級，推動幼托補助擴大，減輕民眾負擔，使人人可以享有優質的照顧，是政府要繼續努力的方向。\n",
        "\n",
        "三年多來，我們推動國防自主、採購先進武器，提升國軍士氣，強化充足戰力。國造高教機的原型機剛剛出廠，國造艦艇也將陸續加入建軍行列。捍衛國土、堅守自由民主，國軍責無旁貸。\n",
        "\n",
        "三年多來，我們積極參與國際，負責任、肯貢獻，成為維護區域和平穩定，不可或缺的良善力量。我們會持續和理念相近的國家攜手，爭取更多實質合作的機會。\n",
        "\n",
        "未來的路線很清楚，目標也很明確。\n",
        "\n",
        "第一，讓國人繼續團結在自由民主的旗幟下，捍衛國家主權。\n",
        "\n",
        "第二，持續壯大台灣，強化經濟實力，讓民富而國強。\n",
        "\n",
        "第三，積極走向世界，克服挑戰，讓中華民國台灣在國際舞台上抬頭挺胸，勇敢自信。\n",
        "\n",
        "上一季，我們的經濟成長率，已經重返亞洲四小龍之首。世界經濟論壇評比我們是四大「超級創新國」之一。我們的高科技和創新產業，也走在世界的最前端。\n",
        "\n",
        "我們的體育選手、我們的技能國手，無數充滿創意的設計師、藝術家，在國際舞台發光發熱，帶給台灣榮耀和驕傲。\n",
        "\n",
        "我們自製的福衛五號，以及福衛七號接連升空，展現太空科技實力。就連人類歷史上第一次觀測到的黑洞影像，也有台灣科學團隊的參與。\n",
        "\n",
        "當我們可以上太空，可以看見五千五百萬光年外的黑洞，那麼，眼前還有什麼挑戰，是我們沒有勇氣面對的？\n",
        "\n",
        "歷史的磨難，會因為我們的韌性，而被轉化為成長力量。天災的挑戰，會因為我們的努力，而成為再生的契機。這塊土地上每一個人的努力，都讓我們的國家，一天一天變得更好、更進步。\n",
        "\n",
        "在國慶日這一天，這塊土地上的所有人民，都團結在自由民主的旗幟下，樂觀面對未來、堅定克服挑戰。天佑台灣，台灣加油，中華民國加油。謝謝大家。\n",
        "\n",
        "'''"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W8GzCk6_E6_J",
        "colab_type": "text"
      },
      "source": [
        "請幫我從蔡英文的國慶演說文分析兩件事:\n",
        "1. 請用len 幫我找出文章的長度?\n",
        "2. 請用count 幫我計算\"台灣\" 出現的次數?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O9h7IQTfE40q",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "6c68a9d5-9343-4cad-e071-6fa4259b769f"
      },
      "source": [
        "len(speech)"
      ],
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2243"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o1QE8br4I8yj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "?speech.count"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IbkPRd0CJC0B",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "b1a81420-b815-44a1-8ff8-f0a627ce739a"
      },
      "source": [
        "speech.count('台灣')"
      ],
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "24"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X9ibyMj1Jl4Y",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "pos = speech.find('台灣')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n7cM8uklJs25",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "5e0c356e-2a3f-4e23-d58a-b55e202aa762"
      },
      "source": [
        "speech[(pos - 10): (pos + 10)]"
      ],
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'國慶日，我和大家說，台灣正處在變局當中。'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0yuqjKwKJy5i",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#speech.split('。')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v775XGQTKgbn",
        "colab_type": "text"
      },
      "source": [
        "## List"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YZeo5evHJ4Is",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "my_list = [1,2,3]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9HbFzX9zKiZf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "my_list = [1,2, 'hello', 'world']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IqTw8urSKtqe",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "ccdd3d08-9ffe-4eae-c274-3968f2e6187c"
      },
      "source": [
        "len(my_list)"
      ],
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EkOmT3mwKzuQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "a = [5,6,7,'s']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BP6HEvjRK5DC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "c3511e90-0168-426e-806a-45455d15997b"
      },
      "source": [
        "a[0]"
      ],
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EUcRc9RjK6pc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "a8de1694-b6e8-4f62-93db-7ae2ca2a0ff4"
      },
      "source": [
        "a[2:4]"
      ],
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[7, 's']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F0XlRjhLK83C",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "bc04b0fd-7ad0-445c-87b8-25ae917e07f4"
      },
      "source": [
        "a[-1]"
      ],
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'s'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b5MVR8H9K-i0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "f4591bc9-49b6-4d6a-9074-cb2d804bf233"
      },
      "source": [
        "a[-2]"
      ],
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "7"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FenD9g3JLAJd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "6748f799-798b-4d11-eb27-275caa4b7b84"
      },
      "source": [
        "a[2:]"
      ],
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[7, 's']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9AOvMLFELCtl",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "a2be2160-4f82-4540-d99b-bb166e47d10d"
      },
      "source": [
        "a[::2]"
      ],
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[5, 7]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AG7Dr1LSLIMS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "fa8cf93f-ec47-4860-faf2-d9814161aebe"
      },
      "source": [
        "len(a)"
      ],
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LkBanvCDLKjg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "b8f834fd-72b7-4bdd-cef4-7749706b7aad"
      },
      "source": [
        "a = [5,6,7,8]\n",
        "a.pop()"
      ],
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "8"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9qEoyXvfLQ79",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "38167c97-dbd5-46b8-815e-ff1d2eb45e6b"
      },
      "source": [
        "a"
      ],
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[5, 6, 7]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 90
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y5OCa9pVLYRK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "?a.append"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_KPJ3L0eLSSV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "a.append(2)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ibUrjgXbL2Ji",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "f7da3535-d688-4a85-c96b-55b3fc2d4694"
      },
      "source": [
        "a"
      ],
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[5, 6, 7, 2]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 93
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZFk5kj1DL2on",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "a.sort()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "caoIPwOUL5ld",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "44f4e0b6-874c-46ab-9996-d781af47318e"
      },
      "source": [
        "a"
      ],
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[2, 5, 6, 7]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 95
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pR1Nw1hXL6KX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "a.reverse()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eFr-T1s_L8rP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "fffa10a1-945e-4104-9808-0cf2b0386e4b"
      },
      "source": [
        "a"
      ],
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[7, 6, 5, 2]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 97
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "srLtQBd0L9YG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 55
        },
        "outputId": "734cc510-0644-4946-bd91-63f3d794442d"
      },
      "source": [
        "print(dir(a))"
      ],
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['__add__', '__class__', '__contains__', '__delattr__', '__delitem__', '__dir__', '__doc__', '__eq__', '__format__', '__ge__', '__getattribute__', '__getitem__', '__gt__', '__hash__', '__iadd__', '__imul__', '__init__', '__init_subclass__', '__iter__', '__le__', '__len__', '__lt__', '__mul__', '__ne__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__reversed__', '__rmul__', '__setattr__', '__setitem__', '__sizeof__', '__str__', '__subclasshook__', 'append', 'clear', 'copy', 'count', 'extend', 'index', 'insert', 'pop', 'remove', 'reverse', 'sort']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UTLUlKmtMCio",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "6e0bc870-c5d4-485b-bfe1-c291c46ce375"
      },
      "source": [
        "list('a')"
      ],
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['a']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 100
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yMJXOEHbMJJ5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "dfbff134-84bf-498b-b2d2-b5690c2d9121"
      },
      "source": [
        "list('hello world')"
      ],
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['h', 'e', 'l', 'l', 'o', ' ', 'w', 'o', 'r', 'l', 'd']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 101
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WsRKjHvCMMid",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "7712cb1a-5382-4b29-d173-4d874607d7cf"
      },
      "source": [
        "'a' in 'hello world'"
      ],
      "execution_count": 102,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 102
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "03FTIPAjMS2k",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "27827f27-f2c3-4e90-b9a3-f915c72a9b5f"
      },
      "source": [
        "'h' in 'hello world'"
      ],
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 103
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1zROTUBvMVii",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "my_list = [1,2,3,'hello']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QTbNrVgUMbUO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "0b44dd9f-1a6b-4260-b458-abbbc0945453"
      },
      "source": [
        "'world' in my_list"
      ],
      "execution_count": 105,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 105
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6g7Mvt-tMdlw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "f5b3ce2a-9b29-4136-93ac-34b8ecfbc4e7"
      },
      "source": [
        "'hello' in my_list"
      ],
      "execution_count": 107,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 107
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5UO_anmNMf4b",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "aa81d41b-7063-4717-8e7d-18d333c24cc7"
      },
      "source": [
        "'world' not in my_list"
      ],
      "execution_count": 108,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 108
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4WzDABM_MmNS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "a = [1,2,3]\n",
        "b = a"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9EhV7Ly_MsYc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "a[1] = 2000"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vCubogBwMuRv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "6343a049-7be8-47fb-b232-5100830615d7"
      },
      "source": [
        "a"
      ],
      "execution_count": 111,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1, 2000, 3]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 111
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xvraSfW7MvW0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "3ee55c08-f331-4d16-98e1-e20bb2e368c6"
      },
      "source": [
        "b"
      ],
      "execution_count": 112,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1, 2000, 3]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 112
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nlmjQ-hZMxz8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "a = [1,2,3]\n",
        "b = a.copy()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lKzaqsZINEp6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "a[1] = 2000 "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8xm8kjOwNH9E",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "8719a1ea-2418-42ce-e70a-4f38fbb33c64"
      },
      "source": [
        "a"
      ],
      "execution_count": 115,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1, 2000, 3]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 115
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6ec55CDKNIuC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "846e92a2-2b16-4f91-940a-647f458f8b11"
      },
      "source": [
        "b"
      ],
      "execution_count": 116,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1, 2, 3]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 116
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OZ5C0yvEOTE3",
        "colab_type": "text"
      },
      "source": [
        "## Dictionary"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fqXxVXsHNJjp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dic = {'a':100, 'b': 'yes', 'c':0.98}"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-FTmIMXJOcbW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "e1ccd7a1-48df-4027-82c0-72d833531c53"
      },
      "source": [
        "dic.keys()"
      ],
      "execution_count": 118,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['a', 'b', 'c'])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 118
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TmG6imzZOeTP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "d9ccc9e0-95c7-42d0-a358-63452680d014"
      },
      "source": [
        "dic.values()"
      ],
      "execution_count": 119,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_values([100, 'yes', 0.98])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 119
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nKrLZ4TZOgnf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "eb232806-ed1b-4e9b-d68b-a72a724a5b48"
      },
      "source": [
        "dic['a']"
      ],
      "execution_count": 120,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "100"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 120
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WxqvYeUrOlBl",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 166
        },
        "outputId": "f71071ee-d0bb-4a27-e9c9-7f2912db5d88"
      },
      "source": [
        "dic['d']"
      ],
      "execution_count": 121,
      "outputs": [
        {
          "output_type": "error",
          "ename": "KeyError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-121-2b1b63ca196d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdic\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'd'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mKeyError\u001b[0m: 'd'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-caeL-3IOpW5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "0eaaa65a-ad68-4800-9f24-41261f59b928"
      },
      "source": [
        "dic.get('a')"
      ],
      "execution_count": 122,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "100"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 122
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qKcO3c3FOxh2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "a6dc1d25-340b-4201-cbc2-d93d8af86dc0"
      },
      "source": [
        "print(dic.get('d'))"
      ],
      "execution_count": 124,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "None\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "INdVnyacOzb3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "b8d36aa2-78dd-4c9d-dc42-4a2db8c81874"
      },
      "source": [
        "dic.get('d', 'default')"
      ],
      "execution_count": 125,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'default'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 125
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_25U1YRwO8Vv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "e8ec4230-51f6-4752-ca8f-4039f7c25ede"
      },
      "source": [
        "dic.get('a', 'default')"
      ],
      "execution_count": 126,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "100"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 126
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BPY3ByIPO-Tc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dic['d'] = 'new'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p1DkXPuhPIhR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "83accef6-6dd3-405e-873b-09db97dc1e3f"
      },
      "source": [
        "dic"
      ],
      "execution_count": 128,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'a': 100, 'b': 'yes', 'c': 0.98, 'd': 'new'}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 128
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4FkhtgZBPJnr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dic2 = {'d': 'newnew', 'e': 'val'}\n",
        "dic.update(dic2)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8C7hDtAAPUGz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "195dccfd-1ed7-4a6d-c526-e6f2b4083676"
      },
      "source": [
        "dic"
      ],
      "execution_count": 130,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'a': 100, 'b': 'yes', 'c': 0.98, 'd': 'newnew', 'e': 'val'}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 130
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "20W3iwMdPfxx",
        "colab_type": "text"
      },
      "source": [
        "我們希望建立一個對話機器人, 機器人裡面內建一個資料問答資料庫, 資料庫的問題跟答案如下所示:\n",
        "1. 問題:我要保保險  答案: 以下是我們推薦的險總類\n",
        "2. 問題:我要貸款  答案: 以下是我們推薦的貸款總類\n",
        "3. 問題:我要查餘額  答案: 以下是您的銀行存款餘額\n",
        "\n",
        "\n",
        "希望你能用這個問答資料庫建立成一個Python 字典, 當使用者輸入問題時, 可以根據問答資料庫回答答案, 但是如果問題不在資料庫之中，請答：我現在還不聰明，等我變聰明以後再回答你\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Sd0WwFNDPUlV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "ed89d2ad-b572-4943-e648-a8c67d375fec"
      },
      "source": [
        "q = input('請輸入你的問題:')\n",
        "print(q)"
      ],
      "execution_count": 131,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "請輸入你讀的問題:哈哈哈\n",
            "哈哈哈\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QMkNC8HFQkX7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "qa = {\n",
        "'我要保保險':'以下是我們推薦的險總類',\n",
        "'我要貸款':'以下是我們推薦的貸款總類',\n",
        "'我要查餘額':'以下是您的銀行存款餘額'\n",
        "}"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EVdU8z8jfQG5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "1af8e7d3-3315-45a1-a526-3f70d18769ea"
      },
      "source": [
        "q = input('請輸入你的問題:')\n",
        "qa.get(q, '我現在還不聰明，等我變聰明以後再回答你')"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "請輸入你的問題:我要查餘額\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'以下是您的銀行存款餘額'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DWj8VQzqgOTB",
        "colab_type": "text"
      },
      "source": [
        "# Tuple"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ti8LEcI8fbmD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "t1 = (1,2,3)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lXoUgLq6gPbf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "t2 = 1,2,3"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_xoJaqtngqbM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "56756cd9-d2c5-4abf-9241-92081b30df51"
      },
      "source": [
        "type(t1)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tuple"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rbSnSe9nguum",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "b5164853-21ee-487b-a089-0cf11f38b404"
      },
      "source": [
        "l1 = [1,2,3]\n",
        "type(l1)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "list"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8VE0hAlBg4AJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 55
        },
        "outputId": "947a74bf-c462-4b11-ea02-da672e73053b"
      },
      "source": [
        "print(dir(l1))"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['__add__', '__class__', '__contains__', '__delattr__', '__delitem__', '__dir__', '__doc__', '__eq__', '__format__', '__ge__', '__getattribute__', '__getitem__', '__gt__', '__hash__', '__iadd__', '__imul__', '__init__', '__init_subclass__', '__iter__', '__le__', '__len__', '__lt__', '__mul__', '__ne__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__reversed__', '__rmul__', '__setattr__', '__setitem__', '__sizeof__', '__str__', '__subclasshook__', 'append', 'clear', 'copy', 'count', 'extend', 'index', 'insert', 'pop', 'remove', 'reverse', 'sort']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-Kku5R0cg-qs",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 55
        },
        "outputId": "0b7037eb-d1db-4205-9f69-5faac3af40d7"
      },
      "source": [
        "print(dir(t1))"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['__add__', '__class__', '__contains__', '__delattr__', '__dir__', '__doc__', '__eq__', '__format__', '__ge__', '__getattribute__', '__getitem__', '__getnewargs__', '__gt__', '__hash__', '__init__', '__init_subclass__', '__iter__', '__le__', '__len__', '__lt__', '__mul__', '__ne__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__rmul__', '__setattr__', '__sizeof__', '__str__', '__subclasshook__', 'count', 'index']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FSdXnD5LhJPu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "t = ('one', 2)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XsEtaPo2heMT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "b9f2e7f1-3426-4780-c3ce-4a6885af8a0b"
      },
      "source": [
        "t.count(2)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PD9GadFghgJD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "c7acaa99-25fd-4d94-c820-4397e5e50a67"
      },
      "source": [
        "len(t)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w80Uy3k1hhkA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "9a6079ba-b67a-4808-dd45-70dabfebaa33"
      },
      "source": [
        "t[0]"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'one'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_TDxJDXrhigh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "5cc6aa10-05aa-450f-abb1-ec7e15e0262b"
      },
      "source": [
        "t[-1]"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_kFcV59zhjh_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 166
        },
        "outputId": "a13235df-caf7-43a3-a921-d49441ca1d0e"
      },
      "source": [
        "t[1] = 200"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-20-77b2e9ee7333>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mt\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m200\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m: 'tuple' object does not support item assignment"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cXTMHVAkhym1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 166
        },
        "outputId": "4b24b04f-adba-4fd6-ce30-63b045c8322c"
      },
      "source": [
        "t.append('123')"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "error",
          "ename": "AttributeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-21-5fa059a504e9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'123'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m: 'tuple' object has no attribute 'append'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o86fLJoeh5Js",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "a = 3\n",
        "b = 2"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZeA3_z2Zh_6F",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "a , b = 3, 2"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V1Hzjx67iDTP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "2bfde369-7de5-44e3-9101-65623ec38661"
      },
      "source": [
        "3,2"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3, 2)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LCA0Pwt2iw3C",
        "colab_type": "text"
      },
      "source": [
        "# Control Flow"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a1fDYazHiD96",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "0cb6acba-183b-4d2c-b842-9acbf568b8ce"
      },
      "source": [
        "if True:\n",
        "                                      print('this is true')"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "this is true\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S7mA__toi21T",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "3d6ca50e-927c-4d33-f2d4-5dcbf21d3d84"
      },
      "source": [
        "a = 5\n",
        "b = 3\n",
        "a > b"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "94aJD6kTjvkA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "665cefe8-322c-4fd3-b03b-1e32ae889c27"
      },
      "source": [
        "a < b"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gctGf4CdjyR-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "1c2cfcd7-7743-4ad5-de0c-c02a2d2f6fef"
      },
      "source": [
        "if True:\n",
        "    print('this is true')"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "this is true\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aKykTgVEj2HM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "if False:\n",
        "    print('this is true')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jqKm0ehrj5B4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "1d03ebc1-6388-42ad-fce1-538c52cdca50"
      },
      "source": [
        "#x = False\n",
        "x = True\n",
        "if x:\n",
        "  print('x was True!')\n",
        "else:\n",
        "  print('I will be printed in any case where x is not true')\n"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "x was True!\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bOs1-0AskA4k",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "6503faf8-1e23-4e4e-d1c3-3df096f14ab9"
      },
      "source": [
        "deposit = 5000\n",
        "if deposit > 30000:\n",
        "  print('Buy iPhone')\n",
        "elif deposit > 20000:\n",
        "  print('Buy Oppo')\n",
        "else:\n",
        "  print('Save More Money')"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Save More Money\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "srNwK3jFkdLb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 191
        },
        "outputId": "2132bc9f-dfa1-47c4-b52b-8d8a1de31296"
      },
      "source": [
        "l = [1,2,3,4,5,6,7,8,9,10]\n",
        "for qoo in l:\n",
        "  print(qoo)"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1\n",
            "2\n",
            "3\n",
            "4\n",
            "5\n",
            "6\n",
            "7\n",
            "8\n",
            "9\n",
            "10\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "beDkcb3pkwD3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 191
        },
        "outputId": "c0e9c2f5-43d6-402a-b2b4-5118f96b1826"
      },
      "source": [
        "for i in  range(1,11):\n",
        "  print(i)"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1\n",
            "2\n",
            "3\n",
            "4\n",
            "5\n",
            "6\n",
            "7\n",
            "8\n",
            "9\n",
            "10\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7d5qnsYKlHTO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "595e9345-1668-4b7f-92d2-f9f6ef84dcad"
      },
      "source": [
        "list_sum = 0\n",
        "for num in range(1,11):\n",
        "  list_sum = list_sum + num\n",
        "list_sum"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "55"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dBFKVLxrlYpr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "901d11b0-ae2c-45ea-8f7a-76924aa1ed14"
      },
      "source": [
        "list_sum = 0\n",
        "for num in range(1,11):\n",
        "  list_sum += num\n",
        "list_sum"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "55"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WER1ggnnlfDP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 208
        },
        "outputId": "5dcb8e7b-64f4-4dca-c4dd-ff758f688a72"
      },
      "source": [
        "a = 'hello world'\n",
        "for l in a:\n",
        "  print(l)"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "h\n",
            "e\n",
            "l\n",
            "l\n",
            "o\n",
            " \n",
            "w\n",
            "o\n",
            "r\n",
            "l\n",
            "d\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dmDjzV7HlnhF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104
        },
        "outputId": "ced2ad18-da4d-40e1-e3d7-afa305d8d054"
      },
      "source": [
        "t = (1,2,3,4,5)\n",
        "for ele in t:\n",
        "  print(ele)"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1\n",
            "2\n",
            "3\n",
            "4\n",
            "5\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zZ2pgchLlrlT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        },
        "outputId": "d1b85c42-2c36-43e1-9873-2f13a6f30dd1"
      },
      "source": [
        "l = [(1,2),(3,4),(5,6)]\n",
        "for t in l:\n",
        "  print(t)"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(1, 2)\n",
            "(3, 4)\n",
            "(5, 6)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hz1mpOjkl0we",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        },
        "outputId": "9effaca0-1a7a-4b2a-a31d-129c239c8148"
      },
      "source": [
        "for t in l:\n",
        "  print(t[1])"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2\n",
            "4\n",
            "6\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "62sKqdm-l5Ai",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        },
        "outputId": "228d86f0-7ade-4786-9cb7-dc1cffca2bfd"
      },
      "source": [
        "for t1,t2 in l:\n",
        "  print(t1, t2)"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1 2\n",
            "3 4\n",
            "5 6\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UgqxYIbrmGI4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        },
        "outputId": "b58544ca-b714-45fb-8262-7b4c23d7cf85"
      },
      "source": [
        "d = {'k1':1, 'k2': 2, 'k3': 3}\n",
        "for k in d:\n",
        "  print(k)"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "k1\n",
            "k2\n",
            "k3\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hH4bcvDVmVbX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        },
        "outputId": "b822afad-43f2-4d6d-9dc1-768fe9fc5862"
      },
      "source": [
        "for k in d:\n",
        "  print(k, d.get(k))"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "k1 1\n",
            "k2 2\n",
            "k3 3\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jb0DSrwTmcoy",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "db1d13c6-95f5-45fd-8c5b-10eedb09386e"
      },
      "source": [
        "d.items()"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_items([('k1', 1), ('k2', 2), ('k3', 3)])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fQsCgUXYmf35",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        },
        "outputId": "eb0026cc-8420-4497-8a4f-1c98ba71db2b"
      },
      "source": [
        "for t in d.items():\n",
        "  print(t)"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "('k1', 1)\n",
            "('k2', 2)\n",
            "('k3', 3)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9lOA1Isxml2O",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        },
        "outputId": "389a637a-0d22-4fce-e300-93a3adedf3ac"
      },
      "source": [
        "for k,v in d.items():\n",
        "  print(k,v)"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "k1 1\n",
            "k2 2\n",
            "k3 3\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JFwdGQzOmq6h",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "outputId": "15b6bc8e-dc16-45fb-fa54-bd039d1ea09d"
      },
      "source": [
        "x = 0\n",
        "while x < 10:\n",
        "  print('x is currently: ',x)\n",
        "  print('x is still less than 10, adding 1 to x')\n",
        "  x+=1\n"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "x is currently:  0\n",
            "x is still less than 10, adding 1 to x\n",
            "x is currently:  1\n",
            "x is still less than 10, adding 1 to x\n",
            "x is currently:  2\n",
            "x is still less than 10, adding 1 to x\n",
            "x is currently:  3\n",
            "x is still less than 10, adding 1 to x\n",
            "x is currently:  4\n",
            "x is still less than 10, adding 1 to x\n",
            "x is currently:  5\n",
            "x is still less than 10, adding 1 to x\n",
            "x is currently:  6\n",
            "x is still less than 10, adding 1 to x\n",
            "x is currently:  7\n",
            "x is still less than 10, adding 1 to x\n",
            "x is currently:  8\n",
            "x is still less than 10, adding 1 to x\n",
            "x is currently:  9\n",
            "x is still less than 10, adding 1 to x\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sYygPqaEnBed",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 381
        },
        "outputId": "3d4591d7-6858-4063-9db4-eb06b07cccf7"
      },
      "source": [
        "x = 0\n",
        "while x < 10:\n",
        "  print('x is currently: ',x)\n",
        "  print('x is still less than 10, adding 1 to x')\n",
        "  x+=1\n",
        "else: # 當while 迴圈成功執行完畢\n",
        "  print('All Done!')"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "x is currently:  0\n",
            "x is still less than 10, adding 1 to x\n",
            "x is currently:  1\n",
            "x is still less than 10, adding 1 to x\n",
            "x is currently:  2\n",
            "x is still less than 10, adding 1 to x\n",
            "x is currently:  3\n",
            "x is still less than 10, adding 1 to x\n",
            "x is currently:  4\n",
            "x is still less than 10, adding 1 to x\n",
            "x is currently:  5\n",
            "x is still less than 10, adding 1 to x\n",
            "x is currently:  6\n",
            "x is still less than 10, adding 1 to x\n",
            "x is currently:  7\n",
            "x is still less than 10, adding 1 to x\n",
            "x is currently:  8\n",
            "x is still less than 10, adding 1 to x\n",
            "x is currently:  9\n",
            "x is still less than 10, adding 1 to x\n",
            "All Done!\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ugHYVz86nNRy",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "485636a1-d618-4ae7-8c46-0b18d54689b7"
      },
      "source": [
        "x = 0\n",
        "while x < 10:\n",
        "  print('x is currently: ',x )\n",
        "  print(' x is still less than 10, adding 1 to x' )\n",
        "  x+=1\n",
        "  if x ==3:\n",
        "    print('x==3')\n",
        "  else:\n",
        "    #continue\n",
        "    #pass\n",
        "    break\n",
        "    print('continuing...' )\n",
        "    "
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "x is currently:  0\n",
            " x is still less than 10, adding 1 to x\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xaLUDwGyo6G9",
        "colab_type": "text"
      },
      "source": [
        "我們希望建立一個對話機器人, 機器人裡面內建一個資料問答資料庫, 資料庫的問題跟答案如下所示:\n",
        "\n",
        "1. 問題:保險 答案: 以下是我們推薦的險總類\n",
        "2. 問題:貸款 答案: 以下是我們推薦的貸款總類\n",
        "3. 問題:存款 答案: 以下是您的銀行存款餘額\n",
        "\n",
        "希望你能用這個問答資料庫建立成一個Python 字典, 當使用者輸入問題時, 只要問題中有部分關鍵字對到問答資料庫的問題,電腦就予以回答 "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GJoPBoTXni3O",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        },
        "outputId": "9d080158-d6dd-403f-cf47-d1d8a6580d2f"
      },
      "source": [
        "qa = {'k1':'v1', 'k2': 'v2'}\n",
        "print(qa.items())\n",
        "for k,v in qa.items():\n",
        "  print(k, v)"
      ],
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "dict_items([('k1', 'v1'), ('k2', 'v2')])\n",
            "k1 v1\n",
            "k2 v2\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zQ3xQ3z_pbuF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "a516a5bc-efc3-4420-dd76-b1d1aa2c65da"
      },
      "source": [
        "'保險' in '我要保保險'"
      ],
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0iT6dJ0YpqCT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "b06861f6-6da3-45ed-eb1d-43531ad84ba6"
      },
      "source": [
        "'早安' in '我要保保險'"
      ],
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h6MQ_CT3psv0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "5bd04c06-a6ff-4ed0-cb73-d6039b6bce60"
      },
      "source": [
        "qa = {\n",
        "'保險':'以下是我們推薦的險總類',\n",
        "'貸款':'以下是我們推薦的貸款總類',\n",
        "'存款':'以下是您的銀行存款餘額'\n",
        "}\n",
        "question = input('請輸入你的問題:')\n",
        "for q in qa:\n",
        "  if q in question:\n",
        "    print(qa.get(q))\n",
        "    break"
      ],
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "請輸入你的問題:請問我的存款有多少?\n",
            "以下是您的銀行存款餘額\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5Wwlj7kAvXOd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "48fa9f35-49bb-4e07-9df2-c5292617877e"
      },
      "source": [
        "qa = {\n",
        "'保險':'以下是我們推薦的險總類',\n",
        "'貸款':'以下是我們推薦的貸款總類',\n",
        "'存款':'以下是您的銀行存款餘額'\n",
        "}\n",
        "question = input('請輸入你的問題:')\n",
        "for q in qa:\n",
        "  if q in question:\n",
        "    print(qa.get(q))\n",
        "    break\n",
        "else:\n",
        "  print('我現在還不聰明,等我變聰明以後再告訴你')"
      ],
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "請輸入你的問題:早安\n",
            "我現在還不聰明,等變聰明以後再告訴你\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Cmmggx0ZwxBc",
        "colab_type": "text"
      },
      "source": [
        "# Function"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8WxTIrr7wBse",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def say_hello():\n",
        "  print('hello world')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Pw2iMijYxLdT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "c6d63ab0-3caa-4589-9529-ed44aa007557"
      },
      "source": [
        "say_hello()"
      ],
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "hello world\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G42vf5oSxPT8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def say_hello(name):\n",
        "  print('hello ' + name)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w1TlnRWYxWZ5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "c6d1b224-afd2-4197-8502-ea4608f1d5d7"
      },
      "source": [
        "say_hello('David')"
      ],
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "hello David\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QRd2sB8jxYw8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "e938169e-cc76-4595-851e-4cd3bf6500d8"
      },
      "source": [
        "say_hello('Marry')"
      ],
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "hello Marry\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ddgrF79Axaog",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def addNum(a, b):\n",
        "  return a+b"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uy31fsWWxoT0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "decc01ff-ef0b-4e9a-cbce-241d6aa1ed6d"
      },
      "source": [
        "addNum(3,5)"
      ],
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "8"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qSkYe7bHxpqB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "fec509b5-f67d-4364-bca6-4c07d4817eae"
      },
      "source": [
        "addNum('Hello', 'World')"
      ],
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'HelloWorld'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ErjgpJioxyY0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "b5226389-b1e6-4536-887f-90e376c3a2a9"
      },
      "source": [
        "addNum([1,2,3], [4,5,6])"
      ],
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1, 2, 3, 4, 5, 6]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I7Fq0Dhgx4Dz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def addNum2(a, b=5):\n",
        "  return a+b"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8b-PEVtex_Ww",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "6dfe0d9f-7a50-4666-a6eb-fd45d124e482"
      },
      "source": [
        "addNum2(3,2)"
      ],
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4PXHRJxfyCs8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "a59a7283-9aba-43c4-b36c-731fb8aeb9df"
      },
      "source": [
        "addNum2(3)"
      ],
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "8"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c5wd4-ikyHC8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 166
        },
        "outputId": "1fab966a-e86a-42fb-9f58-cc4c094b7bde"
      },
      "source": [
        "addNum2()"
      ],
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-90-f8832ca17136>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0maddNum2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m: addNum2() missing 1 required positional argument: 'a'"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HtP8bQoKzexa",
        "colab_type": "text"
      },
      "source": [
        "# Object"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ErYPT-UNyLsa",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "a = 1"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AR7ivnVjy9ED",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "e6f60e6c-c126-4945-9232-f6dfda1665bc"
      },
      "source": [
        "type(a)"
      ],
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "int"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 92
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OU-WOyI4y-yL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 55
        },
        "outputId": "f8875e1e-ac96-415c-8ae9-e28fdcb6cfd2"
      },
      "source": [
        "print(dir(a))"
      ],
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['__abs__', '__add__', '__and__', '__bool__', '__ceil__', '__class__', '__delattr__', '__dir__', '__divmod__', '__doc__', '__eq__', '__float__', '__floor__', '__floordiv__', '__format__', '__ge__', '__getattribute__', '__getnewargs__', '__gt__', '__hash__', '__index__', '__init__', '__init_subclass__', '__int__', '__invert__', '__le__', '__lshift__', '__lt__', '__mod__', '__mul__', '__ne__', '__neg__', '__new__', '__or__', '__pos__', '__pow__', '__radd__', '__rand__', '__rdivmod__', '__reduce__', '__reduce_ex__', '__repr__', '__rfloordiv__', '__rlshift__', '__rmod__', '__rmul__', '__ror__', '__round__', '__rpow__', '__rrshift__', '__rshift__', '__rsub__', '__rtruediv__', '__rxor__', '__setattr__', '__sizeof__', '__str__', '__sub__', '__subclasshook__', '__truediv__', '__trunc__', '__xor__', 'bit_length', 'conjugate', 'denominator', 'from_bytes', 'imag', 'numerator', 'real', 'to_bytes']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YRjm7LAPzBTf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "1261589c-806f-476c-d303-c5e94d1f8db2"
      },
      "source": [
        "a"
      ],
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8ZnIuskVzF3P",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "63a367af-593e-4556-da15-74a36b42e503"
      },
      "source": [
        "id(a)"
      ],
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "10914496"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 95
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AG4cXJDEzILD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class Sample(object):\n",
        "  pass\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KyOroZsrzihO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x = Sample()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y7rc40PbznNo",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "1e1ba8fc-997d-4de5-a9c1-00a3536fac8c"
      },
      "source": [
        "type(x)"
      ],
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "__main__.Sample"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 99
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aU1S8edtzpIM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class Dog(object):\n",
        "  def __init__(self,breed):\n",
        "    self.breed = breed\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pzUqFk8g0nsP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "sam = Dog(breed = 'Lab')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rbIpHITh0yGb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "5fcacc98-1cc2-4597-fb74-fecfdf927495"
      },
      "source": [
        "sam.breed"
      ],
      "execution_count": 102,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Lab'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 102
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xnWcOPHl00gw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "ddc5bd9a-f8c2-4168-de98-cfe2b7741704"
      },
      "source": [
        "type(sam)"
      ],
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "__main__.Dog"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 103
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Hm46E_l502Bf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 55
        },
        "outputId": "81a68160-cf42-4d24-c513-8c443ff971d8"
      },
      "source": [
        "print(dir(sam))"
      ],
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['__class__', '__delattr__', '__dict__', '__dir__', '__doc__', '__eq__', '__format__', '__ge__', '__getattribute__', '__gt__', '__hash__', '__init__', '__init_subclass__', '__le__', '__lt__', '__module__', '__ne__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__setattr__', '__sizeof__', '__str__', '__subclasshook__', '__weakref__', 'breed']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ziqdnlYB04x1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "3c17e327-7345-4ce4-f44b-0458e6e35b04"
      },
      "source": [
        "frank = Dog(breed = 'Huskie')\n",
        "frank.breed"
      ],
      "execution_count": 105,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Huskie'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 105
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mcIjMlR21CGA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class Dog(object):\n",
        "  species = 'mammal' # 類別屬性\n",
        "  def __init__(self,breed,name):\n",
        "    self.breed = breed\n",
        "    self.name = name"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "06EdXvX31TJY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "d1 = Dog('Lab', 'Sam')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HlIHzioV1YdT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 55
        },
        "outputId": "5b40065d-1c4a-40ec-ab64-5d77fd5ab867"
      },
      "source": [
        "print(dir(d1))"
      ],
      "execution_count": 108,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['__class__', '__delattr__', '__dict__', '__dir__', '__doc__', '__eq__', '__format__', '__ge__', '__getattribute__', '__gt__', '__hash__', '__init__', '__init_subclass__', '__le__', '__lt__', '__module__', '__ne__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__setattr__', '__sizeof__', '__str__', '__subclasshook__', '__weakref__', 'breed', 'name', 'species']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ONnelnIM1Z3a",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "ad1460c3-aaac-4feb-ca88-86f130348d33"
      },
      "source": [
        "d1.breed"
      ],
      "execution_count": 109,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Lab'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 109
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6s7Cm0CC1dFj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "fb1c20a1-1a90-481b-fdbf-c35e2d2ec2bd"
      },
      "source": [
        "d1.name"
      ],
      "execution_count": 110,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Sam'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 110
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BFmowW9w1e6R",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "0ce97d46-7fb0-4bda-a9df-6fe0d781932f"
      },
      "source": [
        "d1.species"
      ],
      "execution_count": 111,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'mammal'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 111
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dOfHI6rg1gZ6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "00b69bcb-ce4a-4f4e-a8b9-3a8f71df8caf"
      },
      "source": [
        "Dog.species"
      ],
      "execution_count": 112,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'mammal'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 112
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D4RzdrrM1lEV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class Circle(object):\n",
        "  pi = 3.14\n",
        "  def __init__(self, radius=1):\n",
        "    self.radius = radius\n",
        "  def area(self):\n",
        "    return self.radius * self.radius * Circle.pi\n",
        "  def setRadius(self, radius):\n",
        "    self.radius = radius\n",
        "  def getRadius(self):\n",
        "    return self.radius\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NQi5g6OR2AfC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "c = Circle(1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VESgE3fJ2C_A",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "64c710c6-4846-4680-eb28-7e879b00cd0a"
      },
      "source": [
        "c.getRadius()"
      ],
      "execution_count": 115,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 115
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W0wdyp3M2EsU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "3780c8d7-1c6e-42f5-e492-509e42cc3e07"
      },
      "source": [
        "c.area()"
      ],
      "execution_count": 116,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3.14"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 116
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3Qt1NxT82HMq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "c.setRadius(2)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uvuLHMei2K2D",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "9dca9559-647a-4465-fa2e-adada6bf3060"
      },
      "source": [
        "c.getRadius()"
      ],
      "execution_count": 119,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 119
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M0Iu5DZ92MJi",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "cebff4dd-2aa7-4fb2-d642-fa67fb74a87f"
      },
      "source": [
        "c.area()"
      ],
      "execution_count": 120,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "12.56"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 120
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ojE1UVLE2_WD",
        "colab_type": "text"
      },
      "source": [
        "# File"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qfF1wzRo2P9v",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# w: write, r:read, a: append\n",
        "fid= open('test.txt', 'w')\n",
        "fid.write('Hello\\nWorld')\n",
        "fid.close()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Or5r4_SP3Ml7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "52cfd40e-9fb2-4781-8c6a-018ff8cc569c"
      },
      "source": [
        "fid= open('test.txt', 'r')\n",
        "data = fid.read()\n",
        "print(data)\n",
        "fid.close()"
      ],
      "execution_count": 126,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Hello\n",
            "World\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IoHQGFbH3tSB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "with open('test.txt', 'w') as f:\n",
        "  f.write('hello\\nworld')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jU8tH8O04b5D",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        },
        "outputId": "20f975be-9e45-4718-859a-57fef1430e45"
      },
      "source": [
        "with open('test.txt', 'r') as fid:\n",
        "  for line in fid:\n",
        "    print(\"Line: \" + line)"
      ],
      "execution_count": 130,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Line: hello\n",
            "\n",
            "Line: world\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iDhJGZow4q2f",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 87
        },
        "outputId": "4f66f071-347c-4d22-ec02-8f2146d84e2e"
      },
      "source": [
        "a = '              hello \\n\\n\\n'\n",
        "print(a)"
      ],
      "execution_count": 131,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              hello \n",
            "\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Dc0Z6kxv4-vp",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "944912fa-3b6d-4467-9dbc-40b8de5dd1d0"
      },
      "source": [
        "print(a.strip())"
      ],
      "execution_count": 132,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "hello\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y41vJcw25C6n",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "fbb6f3f7-c629-4395-e730-2e351c803997"
      },
      "source": [
        "with open('test.txt', 'r') as fid:\n",
        "  for line in fid:\n",
        "    print(\"Line: \" + line.strip())"
      ],
      "execution_count": 133,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Line: hello\n",
            "Line: world\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Btbj9IgG5JiU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "with open('test.txt', 'r') as fid:\n",
        "  s = fid.read()\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cS-ZPCFF5Rco",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "42fcb504-3e38-48b9-8343-2604eb2941b9"
      },
      "source": [
        "s"
      ],
      "execution_count": 135,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'hello\\nworld'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 135
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "07ByYOZk5Sgj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "k = 0\n",
        "with open('test.txt', 'r') as fid:\n",
        "  for line in fid:\n",
        "    #k = k + 1\n",
        "    k +=1\n",
        "    #print(\"Line: \" + line.strip())"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xPfCXmzX5fQ1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "065544f9-81b7-4516-9a15-800d13d30604"
      },
      "source": [
        "k"
      ],
      "execution_count": 137,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 137
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wLUldfNL5gch",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "ary = []\n",
        "with open('test.txt', 'r') as fid:\n",
        "  for line in fid:\n",
        "    ary.append(line)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fqqIUGgy5vZ1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "5dc0986f-f878-4c62-c5e9-04503d770fdd"
      },
      "source": [
        "ary"
      ],
      "execution_count": 139,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['hello\\n', 'world']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 139
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BLP1AUVb5wJd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "a171b8c7-49c6-40e2-b2e1-f68328aeed38"
      },
      "source": [
        "len(ary)"
      ],
      "execution_count": 140,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 140
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gv0jhguD50yo",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "c575d651-0ba7-48dc-910d-f137e9578ff2"
      },
      "source": [
        "# pythonic\n",
        "[line for line in open('test.txt', 'r')]"
      ],
      "execution_count": 141,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['hello\\n', 'world']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 141
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CEjrVBtT6MTI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "916d0b3b-6242-43dd-bd32-f32707343027"
      },
      "source": [
        "len([line for line in open('test.txt', 'r')])"
      ],
      "execution_count": 142,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 142
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-XuolmvA6Q0P",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "names = ['陳大名', '王曉明', '蔡依三', '林百禮', '蔡阿貴']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dGh3kjQb6lYS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "24c31faa-e5b4-4ad3-992f-38606348ad96"
      },
      "source": [
        "ary = []\n",
        "for n in names:\n",
        "  if '蔡' in n:\n",
        "    ary.append(n)\n",
        "ary"
      ],
      "execution_count": 144,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['蔡依三', '蔡阿貴']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 144
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HvkEAkxz6u3d",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "a91e004e-c830-4365-e6b6-c06a97685d7d"
      },
      "source": [
        "len(ary)"
      ],
      "execution_count": 145,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 145
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0FeoWFEX6xnD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "aa58e8d3-bd05-418b-a06b-cbdfed44be2c"
      },
      "source": [
        "[n for n in names if '蔡' in n]"
      ],
      "execution_count": 146,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['蔡依三', '蔡阿貴']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 146
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2YHT6c2q65Q7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "601d35b5-811e-4d5b-d56d-78c4b28d9ed3"
      },
      "source": [
        "money = ['$100,000', '$20,000', '30,000', '$50,000', '50000']\n",
        "\n",
        "def money_conversion(m):\n",
        "  return int(m.replace(',', '').strip('$'))\n",
        "\n",
        "ary = []\n",
        "for m in money:\n",
        "  ary.append(money_conversion(m))\n",
        "sum(ary)"
      ],
      "execution_count": 154,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "250000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 154
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ff8zvfdZ7hsZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "f3d4cbc0-5723-4bfb-cd6a-193ec9ea88cb"
      },
      "source": [
        "int('$100,000'.replace(',', '').strip('$'))"
      ],
      "execution_count": 152,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "100000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 152
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MUvBCdsv7Ric",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "e7fc0641-b8df-4dd9-e510-0344109ef154"
      },
      "source": [
        "sum([money_conversion(m) for m in money])"
      ],
      "execution_count": 156,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "250000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 156
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZtXTKGJC8BeT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "ccebeb10-c604-4d5d-e3bb-465690563884"
      },
      "source": [
        "ary = []\n",
        "for m in money:\n",
        "  if money_conversion(m) >= 50000:\n",
        "    ary.append(money_conversion(m))\n",
        "len(ary)"
      ],
      "execution_count": 157,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 157
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K4F8Jhge8N9n",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "b45a2d49-64de-44d0-d4a3-bc9c6ed2b3b2"
      },
      "source": [
        "len([money_conversion(m) for m in money if money_conversion(m) >= 50000])"
      ],
      "execution_count": 159,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 159
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_Mc5yDDw8xXh",
        "colab_type": "text"
      },
      "source": [
        "現在有一川普就職演說文, 現在我們希望各位利用Python 協助我們分析在這篇演說文中, 每個字詞出現的頻率數為何?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SD9TRK7E8Usf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 228
        },
        "outputId": "efc3d3d5-1c4b-42a3-92a0-1edc3df82f2b"
      },
      "source": [
        "! wget https://raw.githubusercontent.com/ywchiu/tibamepy/master/data/trump.txt"
      ],
      "execution_count": 160,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2020-03-07 07:45:27--  https://raw.githubusercontent.com/ywchiu/tibamepy/master/data/trump.txt\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.0.133, 151.101.64.133, 151.101.128.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|151.101.0.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 8736 (8.5K) [text/plain]\n",
            "Saving to: ‘trump.txt’\n",
            "\n",
            "\rtrump.txt             0%[                    ]       0  --.-KB/s               \rtrump.txt           100%[===================>]   8.53K  --.-KB/s    in 0s      \n",
            "\n",
            "2020-03-07 07:45:27 (126 MB/s) - ‘trump.txt’ saved [8736/8736]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YCXQjqXf9h5e",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "c9fde7ee-14b4-4dca-f22d-1407c0549c9d"
      },
      "source": [
        "a = 'this is a book'\n",
        "a.split()"
      ],
      "execution_count": 166,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['this', 'is', 'a', 'book']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 166
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WtUdOPLq9Qcp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "with open('trump.txt', 'r', encoding = 'iso-8859-1') as f:\n",
        "  speech = f.read()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h6zPtuzR9cJY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#speech"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wvJznz0F9c5H",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "li = [1,1,2,2,2,1,3,3,3,2,1,1,1]\n",
        "dic = {}\n",
        "for n in li:\n",
        "  if n not in dic:\n",
        "    dic[n] = 1\n",
        "  else:\n",
        "    dic[n] += 1"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q06xv0KA99aJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "f062ed4a-1049-4563-fd40-0cd2dae9fbfe"
      },
      "source": [
        "dic"
      ],
      "execution_count": 168,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{1: 6, 2: 4, 3: 3}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 168
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zihd1fq8Ge2L",
        "colab_type": "text"
      },
      "source": [
        "# 小任務"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A-LVfLByGl6M",
        "colab_type": "text"
      },
      "source": [
        "## 方法一"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Eocgcw1F9-2u",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "with open('trump.txt', 'r', encoding = 'iso-8859-1') as f:\n",
        "  speech = f.read()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d9SS3YahGq39",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "words = speech.lower().split()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n8ikMpS1Gr8f",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dic = {}\n",
        "for w in words:\n",
        "  if w not in dic:\n",
        "    dic[w] = 1\n",
        "  else:\n",
        "    dic[w] = dic[w] + 1"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K4gHBlVMHKo-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#dic"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LWFyqpB3HauY",
        "colab_type": "text"
      },
      "source": [
        "## 方法二"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cV0UhvkYHLgD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dic = {}\n",
        "for w in words:\n",
        "  dic[w] = dic.get(w,0) + 1\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NnmKkvHnHxim",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#dic"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vHlAZMVAH70Z",
        "colab_type": "text"
      },
      "source": [
        "## 方法三"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_IbogYQJHyEm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dic = {}\n",
        "for w in set(words):\n",
        "  dic[w] = words.count(w)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8NKYMESrH-0B",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#dic"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m200cyNSJlJN",
        "colab_type": "text"
      },
      "source": [
        "# 如何排序字典"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9HGL0_UHJp6i",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "?sorted"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6EGxN8zQJ6jx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#dic.items()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AdsyxqpgKCkQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "fb40f859-e1d1-4afd-b2da-ed4813f7955d"
      },
      "source": [
        "def first(e):\n",
        "  return e[0]\n",
        "\n",
        "a = 'hello'\n",
        "first(a)"
      ],
      "execution_count": 190,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'h'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 190
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nidaZg92KV5Y",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "67e49087-9473-470f-f40c-5538f38ca11d"
      },
      "source": [
        "def first2(e): return e[0]\n",
        "first2(a)"
      ],
      "execution_count": 191,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'h'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 191
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V2gjsxzWKdOm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "d6c6bd36-c9bd-4acf-8996-ce8258235970"
      },
      "source": [
        "first3 = lambda e: e[0]\n",
        "first3(a)"
      ],
      "execution_count": 192,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'h'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 192
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ai8uubnAKnGw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 55
        },
        "outputId": "d415de7b-74f7-4d6a-9bdb-19c1d2c1ba76"
      },
      "source": [
        "dic.items()"
      ],
      "execution_count": 193,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_items([('homes', 1), ('trump', 1), ('again.', 8), ('can', 1), ('carry', 1), ('seek', 2), ('carter,', 1), ('it', 9), ('gangs', 1), ('they', 5), ('creator.', 1), ('reality', 1), ('delivery', 2), ('starting', 1), ('rich', 1), ('joined', 1), ('thought', 1), ('power', 2), ('refusing', 1), ('black', 1), ('urban', 1), ('historic', 1), ('another', 1), ('destroying', 1), ('large,', 1), ('sprawl', 1), ('freedoms,', 1), ('obama', 2), ('remembered', 1), ('gathered', 1), ('lead', 1), ('beautiful', 1), ('guide', 1), ('voice,', 1), ('benefit', 1), ('open', 1), ('ignored', 1), ('while', 6), ('mysteries', 1), ('years', 1), ('whether', 3), ('citizens.', 1), ('allowing', 1), ('changes', 1), ('immigration,', 1), ('establishment', 1), ('lift', 1), ('wealthy', 1), ('tombstones', 1), ('understand', 1), ('pride', 1), ('prepared', 2), ('protect', 1), ('time', 2), ('success.', 1), ('aid', 1), ('law', 1), ('robbed', 1), ('exists:', 1), ('dreams,', 2), ('earth', 1), ('mountain', 1), ('technologies', 1), ('ceremony,', 1), ('did', 1), ('rulers', 1), ('what', 1), ('power.', 1), ('entire', 1), ('families', 1), ('two', 1), ('affairs,', 1), ('total', 1), ('subsidized', 1), ('winning', 2), ('down.', 1), ('magnificent.', 1), ('glorious', 2), ('jobs.', 2), ('on', 6), ('so', 3), ('capital', 1), ('bridges,', 1), ('¡§how', 1), ('themselves.', 1), ('workers', 2), ('great', 6), ('children', 1), (\"america's\", 1), ('space,', 1), ('gather', 1), ('challenge', 1), ('became', 1), ('left,', 1), ('flag.', 1), ('goodness', 1), ('depletion', 1), ('unrealized', 1), ('free', 1), ('rules:', 1), ('pleasant', 1), ('action.', 1), ('small', 2), ('administration', 1), ('carnage', 1), ('red', 1), ('government', 2), ('poverty', 1), ('rediscover', 1), ('or', 4), ('assembled', 1), ('however,', 1), ('transfer', 1), ('hardships.', 1), ('years,', 1), ('trillions', 1), ('alliances', 1), ('courage', 1), ('patriots,', 1), ('d.c.', 2), ('another,', 1), ('reasonable', 1), ('fallen', 1), ('public.', 1), ('obama,', 1), ('came', 1), ('listening', 1), ('disease,', 1), ('loyalty', 2), ('2017,', 1), ('cities;', 1), ('against', 1), ('america,', 4), ('prejudice.', 1), ('put', 1), ('hour', 1), ('should', 1), ('families.', 1), ('j.', 1), ('group', 1), ('own', 1), ('20th', 1), ('every', 7), ('tomorrow.', 1), ('spent', 1), ('unite', 1), ('heard', 1), ('near', 1), ('a', 15), ('prosperity', 1), ('factories', 3), ('heart,', 1), ('made', 2), ('govern', 1), ('again,', 1), ('january', 2), ('celebration.', 1), ('because', 2), ('washington', 1), ('when', 3), ('dreams', 1), ('accept', 1), ('no', 6), ('radical', 1), ('little', 1), ('neighborhoods', 1), ('wealth', 1), ('we¡¦ve', 2), ('and', 72), ('everyone', 4), ('expense', 1), ('remarks', 1), ('nation.', 1), ('industry', 1), ('my', 1), ('itself,', 1), ('bigger.', 1), ('unlock', 1), ('pain', 1), ('leaves', 1), ('as', 6), ('white,', 1), ('far,', 1), ('hear', 1), ('souls,', 1), ('hire', 1), ('this,', 1), ('tell', 1), ('which', 4), ('i', 3), ('must', 3), ('world:', 1), ('god', 2), ('armies', 1), ('prosper', 1), ('people', 6), ('future.', 1), ('american', 11), ('debate', 1), ('throughout', 1), ('even', 2), ('united', 2), ('address', 2), ('empty', 1), ('stir', 1), ('students', 1), ('steps', 1), ('islamic', 1), ('thank', 2), ('us', 1), ('wealth.', 2), ('celebrate', 1), ('not', 10), ('struggling', 1), ('united,', 1), ('through', 1), ('families,', 1), ('talk', 2), ('triumphs;', 1), ('flush', 1), ('strength,', 1), ('city,', 1), ('shine', 1), ('minds', 1), ('love', 1), ('sights,', 1), ('birth', 1), ('city', 1), ('middle', 1), ('words:', 1), ('by', 6), ('and,', 2), ('national', 2), ('oath', 2), ('forget:', 1), ('success', 1), ('dreams;', 1), ('your', 11), ('wonderful', 1), ('defended', 1), ('restore', 1), ('children,', 1), ('decision', 1), ('our', 48), ('country', 6), ('that', 8), ('new', 6), ('job', 1), ('overseas', 1), ('ever', 1), ('come.', 1), ('fill', 1), ('striving.', 1), ('like', 2), ('cost.', 1), ('women', 2), ('trapped', 1), ('form', 1), ('we,', 1), ('over.', 1), ('donald', 1), ('determine', 1), ('bible', 1), ('match', 1), ('with', 8), ('dreams.', 1), ('prospered', 1), ('part', 1), ('friday,', 1), ('sad', 1), ('into', 1), ('land.', 2), ('industry;', 1), ('together', 1), ('most', 1), ('will', 40), ('never', 5), ('make', 5), ('now.', 2), ('countries', 3), ('over', 1), ('americans,', 2), ('now', 3), ('cannot', 1), ('stand', 1), ('too', 3), ('nebraska,', 1), ('millennium,', 1), ('buy', 1), ('big', 1), ('enforcement', 1), ('office', 1), ('party', 2), ('disrepair', 1), ('day', 2), ('challenges.', 1), ('never,', 1), ('you', 7), ('work', 1), ('along', 1), ('breath', 2), ('build', 1), ('up', 1), ('these', 3), ('horizon.', 1), ('be', 12), ('government,', 1), ('back', 6), ('bring', 4), ('dream', 1), ('class', 1), ('looking', 1), ('impose', 1), ('highways,', 1), ('transferring', 2), ('crime', 1), ('tells', 1), ('disappeared', 1), ('start', 1), ('making', 1), ('today', 4), ('potential.', 1), ('action', 1), ('millions', 3), ('giving', 1), ('capital,', 2), ('wealth,', 1), ('ocean', 1), ('ripped', 1), ('american.', 1), ('own;', 1), ('bedrock', 1), ('their', 11), ('let', 3), ('done.', 2), ('lives', 1), ('arrives', 1), ('you,', 3), ('triumphs', 1), ('complaining', 1), ('knowledge;', 1), ('the', 71), ('rewards', 1), ('matters', 1), ('first.', 2), ('spirit', 1), ('military;', 1), ('taxes,', 1), ('shuttered', 1), ('strong', 1), ('it¡¦s', 1), ('always', 2), ('ocean,', 1), ('fight', 2), ('friendship', 1), ('windswept', 1), ('citizens', 2), ('on,', 1), ('detroit', 1), ('justice', 1), ('fear', 1), ('moment', 2), ('today¡¦s', 1), ('finally,', 1), ('20,', 1), ('forgotten', 2), ('living', 1), (\"we've\", 1), ('controls', 1), ('roads,', 1), ('solidarity.', 1), ('america.', 3), ('old', 2), ('salute', 1), ('shores,', 1), ('at', 5), ('in', 14), ('crucial', 1), ('world', 4), ('nation¡¦s', 3), ('off', 1), ('room', 1), ('is', 21), ('terrorism,', 1), ('protected', 3), ('first', 1), ('this', 9), ('way', 1), ('for', 17), ('many', 3), ('men', 2), ('exists', 1), ('each', 1), ('become', 1), ('way.', 1), ('nation;', 1), ('goodwill', 1), ('heal', 1), ('schools', 1), ('confront', 1), ('then', 1), ('its', 3), ('from', 11), ('share', 2), ('earth.', 1), ('course', 1), ('tunnels,', 1), ('states', 2), ('mothers', 1), ('scattered', 1), ('long', 1), ('products,', 1), ('define', 1), ('enriched', 1), ('borders', 2), ('rebuild', 1), ('defend', 1), ('patriotism,', 1), ('gracious', 1), ('disagreements', 1), ('totally', 1), ('nations', 2), ('thrive', 1), ('now,', 1), ('proud', 1), ('decades,', 1), ('victories', 1), ('cash,', 1), ('left', 2), ('rebuilding', 1), ('simple', 1), ('civilized', 1), ('confidence', 1), ('very', 2), ('forward,', 1), ('protected,', 1), ('seen', 1), ('labor.', 1), ('young', 1), ('only', 2), ('borne', 1), ('railways', 1), ('have', 5), ('americans.', 1), ('understanding', 1), ('but', 13), ('one', 7), ('think', 1), ('longer', 1), ('eradicate', 1), ('washington,', 2), ('politics', 1), ('tens', 1), ('unstoppable.', 1), ('doing', 1), ('there', 3), ('moment:', 1), ('celebrated', 1), ('heart', 3), ('out', 1), ('completely', 1), ('deprived', 1), ('brown', 1), ('look', 1), ('lady', 1), ('peaceful', 1), ('special', 1), ('decay.', 1), ('effort', 1), ('god¡¦s', 1), ('redistributed', 1), ('energies,', 1), ('ones', 1), ('harness', 1), ('much', 1), ('anyone', 1), ('fellow', 1), ('all', 12), ('allegiance', 2), ('world.', 1), ('jobs', 2), ('welfare', 1), ('interests', 1), ('sky,', 1), ('pursue', 1), ('center', 1), ('watching', 1), ('miseries', 1), ('here', 3), ('been', 4), ('yes,', 1), ('we', 45), ('to', 37), ('politicians', 2), ('openly,', 1), ('going', 1), ('hall', 1), ('honestly,', 1), ('almighty', 1), ('do', 3), ('four', 1), ('trade,', 1), ('grateful', 1), ('unity.¡¨', 1), ('ravages', 1), ('are', 14), ('landscape', 1), ('who', 1), ('long,', 1), ('ready', 1), ('airports,', 1), ('take', 1), ('2017', 1), ('inaugural', 2), ('good', 2), ('same', 6), ('importantly,', 1), ('before.', 2), ('example', 1), ('bleed', 1), ('was', 1), ('meaning.', 1), ('conviction:', 1), ('god.', 1), ('hands', 1), ('right', 5), ('infrastructure', 1), ('president', 6), ('day.', 1), ('rusted-out', 1), ('blood', 1), ('here,', 1), ('victories;', 1), ('live', 1), ('dollars', 1), ('foreign', 3), ('speak', 1), ('infused', 1), ('an', 3), ('americans', 1), ('stolen', 1), ('drugs', 1), ('anyone,', 1), ('chief', 1), ('country.', 2), ('face', 2), ('get', 2), ('across', 5), ('citizens,', 1), ('us,', 1), ('it.', 1), ('plains', 1), ('vision', 1), ('longer.', 1), ('body', 1), ('home,', 1), ('america', 10), ('merely', 1), ('people.', 3), ('together,', 3), ('fail.', 1), ('life', 2), ('closed.', 1), ('orderly', 1), ('movement', 2), ('different', 1), ('protected.', 1), ('other.', 1), ('michelle', 1), ('child', 1), ('remember', 1), ('mountain,', 1), ('other', 4), ('education', 1), ('divisions.', 1), ('protection', 1), ('night', 1), ('¡v', 13), ('roberts,', 1), ('system,', 1), ('strength.', 1), ('behind.', 1), ('righteous', 1), ('promise', 1), ('hopes,', 1), ('destiny.', 2), ('stealing', 1), ('constantly', 1), ('industries', 1), ('enjoy', 1), ('reaped', 1), ('past.', 1), ('country,', 1), ('stops', 2), ('truly', 1), ('soldiers', 1), ('safe', 2), ('reinforce', 1), ('companies,', 1), ('pain.', 1), ('anything', 1), ('rather', 1), ('belongs', 2), ('borders.', 1), ('military', 1), ('you.', 2), ('one,', 1), ('clinton,', 1), ('upon', 1), ('decree', 1), ('wisdom', 1), ('born', 1), ('follow', 1), ('bless', 2), ('issuing', 1), ('transition.', 1), ('has', 6), ('flourished', 1), ('demands', 1), ('power,', 1), ('follow.', 1), ('likes', 1), ('about', 2), ('serve', 1), ('of', 49), ('want', 1), ('controlled', 1), ('bush,', 1), ('just', 1), ('nation', 4), ('forever', 1), ('inner', 1)])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 193
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p8CLIrKlINPt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "swd = sorted(dic.items(), key=lambda item: item[1], reverse=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "shNFkxhvK6SB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 191
        },
        "outputId": "7e535238-3456-471d-eded-2b54a3ee7698"
      },
      "source": [
        "swd[0:10]"
      ],
      "execution_count": 197,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('and', 72),\n",
              " ('the', 71),\n",
              " ('of', 49),\n",
              " ('our', 48),\n",
              " ('we', 45),\n",
              " ('will', 40),\n",
              " ('to', 37),\n",
              " ('is', 21),\n",
              " ('for', 17),\n",
              " ('a', 15)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 197
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n_AghrwsLJ8M",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 191
        },
        "outputId": "c47ad23a-c844-4b6f-b298-c4bfa22865d9"
      },
      "source": [
        "for k,v in swd[0:10]:\n",
        "  print(k,v)"
      ],
      "execution_count": 198,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "and 72\n",
            "the 71\n",
            "of 49\n",
            "our 48\n",
            "we 45\n",
            "will 40\n",
            "to 37\n",
            "is 21\n",
            "for 17\n",
            "a 15\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GB089UHALp4P",
        "colab_type": "text"
      },
      "source": [
        "# 移除停用詞"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "641DTNe5L4N4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "with open('trump.txt', 'r', encoding = 'iso-8859-1') as f:\n",
        "  speech = f.read()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jqrH0ybnL6cQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "words = speech.lower().split()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7SpCqzUOLQqV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "stopwords = [\"a\", \"about\", \"above\", \"above\", \"across\", \"after\", \"afterwards\", \"again\", \"against\", \"all\", \"almost\", \"alone\", \"along\", \"already\", \"also\",\"although\",\"always\",\"am\",\"among\", \"amongst\", \"amoungst\", \"amount\",  \"an\", \"and\", \"another\", \"any\",\"anyhow\",\"anyone\",\"anything\",\"anyway\", \"anywhere\", \"are\", \"around\", \"as\",  \"at\", \"back\",\"be\",\"became\", \"because\",\"become\",\"becomes\", \"becoming\", \"been\", \"before\", \"beforehand\", \"behind\", \"being\", \"below\", \"beside\", \"besides\", \"between\", \"beyond\", \"bill\", \"both\", \"bottom\",\"but\", \"by\", \"call\", \"can\", \"cannot\", \"cant\", \"co\", \"con\", \"could\", \"couldnt\", \"cry\", \"de\", \"describe\", \"detail\", \"do\", \"done\", \"down\", \"due\", \"during\", \"each\", \"eg\", \"eight\", \"either\", \"eleven\",\"else\", \"elsewhere\", \"empty\", \"enough\", \"etc\", \"even\", \"ever\", \"every\", \"everyone\", \"everything\", \"everywhere\", \"except\", \"few\", \"fifteen\", \"fify\", \"fill\", \"find\", \"fire\", \"first\", \"five\", \"for\", \"former\", \"formerly\", \"forty\", \"found\", \"four\", \"from\", \"front\", \"full\", \"further\", \"get\", \"give\", \"go\", \"had\", \"has\", \"hasnt\", \"have\", \"he\", \"hence\", \"her\", \"here\", \"hereafter\", \"hereby\", \"herein\", \"hereupon\", \"hers\", \"herself\", \"him\", \"himself\", \"his\", \"how\", \"however\", \"hundred\", \"ie\", \"if\", \"in\", \"inc\", \"indeed\", \"interest\", \"into\", \"is\", \"it\", \"its\", \"itself\", \"keep\", \"last\", \"latter\", \"latterly\", \"least\", \"less\", \"ltd\", \"made\", \"many\", \"may\", \"me\", \"meanwhile\", \"might\", \"mill\", \"mine\", \"more\", \"moreover\", \"most\", \"mostly\", \"move\", \"much\", \"must\", \"my\", \"myself\", \"name\", \"namely\", \"neither\", \"never\", \"nevertheless\", \"next\", \"nine\", \"no\", \"nobody\", \"none\", \"noone\", \"nor\", \"not\", \"nothing\", \"now\", \"nowhere\", \"of\", \"off\", \"often\", \"on\", \"once\", \"one\", \"only\", \"onto\", \"or\", \"other\", \"others\", \"otherwise\", \"our\", \"ours\", \"ourselves\", \"out\", \"over\", \"own\",\"part\", \"per\", \"perhaps\", \"please\", \"put\", \"rather\", \"re\", \"same\", \"see\", \"seem\", \"seemed\", \"seeming\", \"seems\", \"serious\", \"several\", \"she\", \"should\", \"show\", \"side\", \"since\", \"sincere\", \"six\", \"sixty\", \"so\", \"some\", \"somehow\", \"someone\", \"something\", \"sometime\", \"sometimes\", \"somewhere\", \"still\", \"such\", \"system\", \"take\", \"ten\", \"than\", \"that\", \"the\", \"their\", \"them\", \"themselves\", \"then\", \"thence\", \"there\", \"thereafter\", \"thereby\", \"therefore\", \"therein\", \"thereupon\", \"these\", \"they\", \"thick\", \"thin\", \"third\", \"this\", \"those\", \"though\", \"three\", \"through\", \"throughout\", \"thru\", \"thus\", \"to\", \"together\", \"too\", \"top\", \"toward\", \"towards\", \"twelve\", \"twenty\", \"two\", \"un\", \"under\", \"until\", \"up\", \"upon\", \"us\", \"very\", \"via\", \"was\", \"we\", \"well\", \"were\", \"what\", \"whatever\", \"when\", \"whence\", \"whenever\", \"where\", \"whereafter\", \"whereas\", \"whereby\", \"wherein\", \"whereupon\", \"wherever\", \"whether\", \"which\", \"while\", \"whither\", \"who\", \"whoever\", \"whole\", \"whom\", \"whose\", \"why\", \"will\", \"with\", \"within\", \"without\", \"would\", \"yet\", \"you\", \"your\", \"yours\", \"yourself\", \"yourselves\", \"the\"]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TZPaHXVsLpN3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dic = {}\n",
        "for w in words:\n",
        "  if (w not in stopwords) and (len(w) > 2):\n",
        "    dic[w] = dic.get(w,0) + 1\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b8uBStX7LoOk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "swd = sorted(dic.items(), key=lambda item: item[1], reverse=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y1RJ3JKTMFlE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "outputId": "269c82e2-3b9d-4994-ee23-d4896fc5c698"
      },
      "source": [
        "swd[0:20]"
      ],
      "execution_count": 212,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('american', 11),\n",
              " ('america', 10),\n",
              " ('again.', 8),\n",
              " ('president', 6),\n",
              " ('people', 6),\n",
              " ('great', 6),\n",
              " ('country', 6),\n",
              " ('new', 6),\n",
              " ('right', 5),\n",
              " ('make', 5),\n",
              " ('america,', 4),\n",
              " ('world', 4),\n",
              " ('today', 4),\n",
              " ('nation', 4),\n",
              " ('bring', 4),\n",
              " ('people.', 3),\n",
              " ('together,', 3),\n",
              " ('you,', 3),\n",
              " ('nation¡¦s', 3),\n",
              " ('factories', 3)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 212
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "662qSK7iMjmE",
        "colab_type": "text"
      },
      "source": [
        "# Counter\n",
        "- https://docs.python.org/3.7/library/collections.html"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-H7O4fvuMG-B",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "with open('trump.txt', 'r', encoding = 'iso-8859-1') as f:\n",
        "  speech = f.read()\n",
        "words = [w for w in speech.lower().split() if (w not in stopwords) and (len(w)>2)]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WWMlIoqMM36V",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from collections import Counter\n",
        "c = Counter(words)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tdoiZZyPM7l7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 191
        },
        "outputId": "0f7e7ccd-2602-4a5b-88f7-38194c5398c9"
      },
      "source": [
        "c.most_common(10)"
      ],
      "execution_count": 218,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('american', 11),\n",
              " ('america', 10),\n",
              " ('again.', 8),\n",
              " ('president', 6),\n",
              " ('people', 6),\n",
              " ('great', 6),\n",
              " ('country', 6),\n",
              " ('new', 6),\n",
              " ('right', 5),\n",
              " ('make', 5)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 218
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NutesVSCM-Cb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 191
        },
        "outputId": "28143705-934c-4455-c9c9-54dcff4016b0"
      },
      "source": [
        "Counter([w for w in speech.lower().split() if (w not in stopwords) and (len(w)>2)]).most_common(10)"
      ],
      "execution_count": 220,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('american', 11),\n",
              " ('america', 10),\n",
              " ('again.', 8),\n",
              " ('president', 6),\n",
              " ('people', 6),\n",
              " ('great', 6),\n",
              " ('country', 6),\n",
              " ('new', 6),\n",
              " ('right', 5),\n",
              " ('make', 5)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 220
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dCkxHzwrPbR8",
        "colab_type": "text"
      },
      "source": [
        "# 安裝第三方套件"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YTDB7m85Phgx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 89
        },
        "outputId": "00667b30-e236-41c0-cea6-577be82f285a"
      },
      "source": [
        "! pip install wordcloud"
      ],
      "execution_count": 222,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: wordcloud in /usr/local/lib/python3.6/dist-packages (1.5.0)\n",
            "Requirement already satisfied: numpy>=1.6.1 in /usr/local/lib/python3.6/dist-packages (from wordcloud) (1.17.5)\n",
            "Requirement already satisfied: pillow in /usr/local/lib/python3.6/dist-packages (from wordcloud) (6.2.2)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fTvU8nt4NT9S",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 215
        },
        "outputId": "c636f6dd-6970-4086-9c2c-b8392dc7c530"
      },
      "source": [
        "from wordcloud import WordCloud\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "text = open('trump.txt', 'r', encoding='iso-8859-1').read()\n",
        "\n",
        "wordcloud = WordCloud().generate(text)\n",
        "\n",
        "plt.imshow(wordcloud, interpolation='bilinear')\n",
        "plt.axis(\"off\")"
      ],
      "execution_count": 224,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(-0.5, 399.5, 199.5, -0.5)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 224
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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XhH+UICEAmUmRfuIhpG0TbpQJFpZ3Ao7sqXHMUi86CPBvLBBvVRGmiTk8gD08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\nGP0DNN96a49oXtJu48/PU3juOclOtLyM0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wj4vd/x9yEY1to3KViDCJESJh5h4hDEDkHc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6TfH446TH9tMev7SanzZzRVKje1FK0bh0CrlcPFBK0V6YRDMTyDDATGYIWjVCp4n0XYJmldBp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\ncYhDHOJjxP8HePtlZWXi63AAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V8kGXYVdQe2E",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 124
        },
        "outputId": "43c02fb0-2068-4071-b680-d977634f9a00"
      },
      "source": [
        "! pip install SpeechRecognition"
      ],
      "execution_count": 225,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting SpeechRecognition\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/26/e1/7f5678cd94ec1234269d23756dbdaa4c8cfaed973412f88ae8adf7893a50/SpeechRecognition-3.8.1-py2.py3-none-any.whl (32.8MB)\n",
            "\u001b[K     |████████████████████████████████| 32.8MB 123kB/s \n",
            "\u001b[?25hInstalling collected packages: SpeechRecognition\n",
            "Successfully installed SpeechRecognition-3.8.1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7jb_X0gTQpMi",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "07bf3cce-666c-4070-ed8e-4a9b49bea211"
      },
      "source": [
        "import speech_recognition as sr\n",
        "from os import path\n",
        "\n",
        "AUDIO_FILE = \"myvoice.wav\"\n",
        "r = sr.Recognizer()\n",
        "with sr.AudioFile(AUDIO_FILE) as source:\n",
        "    audio = r.record(source) \n",
        "\n",
        "print(r.recognize_google(audio, language='zh_TW'))"
      ],
      "execution_count": 229,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "大家好今天很高兴来到这边\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tUSrPYxgS0ni",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "作業 (Python 基礎)\n",
        "- 1 請使用 pip 將 jieba 安裝在自己的電腦上\n",
        "- 2 閱讀jieba 相關的文件，了解jieba 的用法\n",
        "- 3 請將蔡英文演說文 (https://raw.githubusercontent.com/ywchiu/tibamedl/master/Data/speech_tsai.txt) 下載存入到自己電腦\n",
        "- 4 請使用python 的檔案操作方法讀取新聞\n",
        "- 5 請使用 jieba 將演說文切詞\n",
        "- 6 請統計該演說文最常出現的十大字詞"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NH5JCyoBPeyv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}