Country.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Hello, Colaboratory",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "[View in Colaboratory](https://colab.research.google.com/github/buntys2010/chatbot-facebook-nodejs/blob/master/Country.ipynb)"
      ]
    },
    {
      "metadata": {
        "id": "9J7p406abzgl",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Welcome to Colaboratory!\n",
        "\n",
        "Colaboratory is a Google research project created to help disseminate machine learning education and research. It's a Jupyter notebook environment that requires no setup to use and runs entirely in the cloud.\n",
        "\n",
        "Colaboratory notebooks are stored in [Google Drive](https://drive.google.com) and can be shared just as you would with Google Docs or Sheets. Colaboratory is free to use.\n",
        "\n",
        "For more information, see our [FAQ](https://research.google.com/colaboratory/faq.html)."
      ]
    },
    {
      "metadata": {
        "id": "rTX3heEtu0b2",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Local runtime support\n",
        "\n",
        "Colab also supports connecting to a Jupyter runtime on your local machine. For more information, see our [documentation](https://research.google.com/colaboratory/local-runtimes.html)."
      ]
    },
    {
      "metadata": {
        "id": "8iU2gnJFItM1",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Python 3\n",
        "\n",
        "Colaboratory supports both Python2 and Python3 for code execution. \n",
        "\n",
        "* When creating a new notebook, you'll have the choice between Python 2 and Python 3.\n",
        "* You can also change the language associated with a notebook; this information will be written into the `.ipynb` file itself, and thus will be preserved for future sessions."
      ]
    },
    {
      "metadata": {
        "id": "na0VvPXmYKp1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "b4b4f6e5-4453-454e-c329-6569adb5f66e"
      },
      "cell_type": "code",
      "source": [
        "import sys\n",
        "print('Hello, Colaboratory from Python {}!'.format(sys.version_info[0]))"
      ],
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Hello, Colaboratory from Python 3!\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "9wi5kfGdhK0R",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## TensorFlow execution"
      ]
    },
    {
      "metadata": {
        "id": "S9GW-n-oYWIj",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Colaboratory allows you to execute TensorFlow code in your browser with a single click. The example below adds two matrices.\n",
        "\n",
        "$\\begin{bmatrix}\n",
        "  1. & 1. & 1. \\\\\n",
        "  1. & 1. & 1. \\\\\n",
        "\\end{bmatrix} +\n",
        "\\begin{bmatrix}\n",
        "  1. & 2. & 3. \\\\\n",
        "  4. & 5. & 6. \\\\\n",
        "\\end{bmatrix} =\n",
        "\\begin{bmatrix}\n",
        "  2. & 3. & 4. \\\\\n",
        "  5. & 6. & 7. \\\\\n",
        "\\end{bmatrix}$"
      ]
    },
    {
      "metadata": {
        "id": "mwdQ1INEZKkb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "072fd1ae-90a2-4a17-d6fe-8476e5aa790f"
      },
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "\n",
        "with tf.Session():\n",
        "  input1 = tf.constant(1.0, shape=[2, 3])\n",
        "  input2 = tf.constant(np.reshape(np.arange(1.0, 7.0, dtype=np.float32), (2, 3)))\n",
        "  output = tf.add(input1, input2)\n",
        "  result = output.eval()\n",
        "\n",
        "result"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[2., 3., 4.],\n",
              "       [5., 6., 7.]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 62
        }
      ]
    },
    {
      "metadata": {
        "id": "yv2XIwi5hQ_g",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Visualization"
      ]
    },
    {
      "metadata": {
        "id": "rYs5mx2JZkmy",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Colaboratory includes widely used libraries like [matplotlib](https://matplotlib.org/), simplifying visualization."
      ]
    },
    {
      "metadata": {
        "id": "xqrc5C-IaA5J",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "1a724e2a-ba64-40c9-ad9a-79ac4fff48f6"
      },
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "x = np.arange(20)\n",
        "y = [x_i + np.random.randn(1) for x_i in x]\n",
        "a, b = np.polyfit(x, y, 1)\n",
        "_ = plt.plot(x, y, 'o', np.arange(20), a*np.arange(20)+b, '-')"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7ff29f7516d8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "AN_LRQ9NkOjs",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Want to use a new library?  `pip install` it. For recipes to import commonly used libraries, refer to the [importing libraries example notebook](/notebooks/snippets/importing_libraries.ipynb)"
      ]
    },
    {
      "metadata": {
        "id": "FlQq0SUepQbd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 249
        },
        "outputId": "ec37b66f-54b8-4e47-aad4-a71e0f365cfd"
      },
      "cell_type": "code",
      "source": [
        "# Only needs to be run once at the top of the notebook.\n",
        "!pip install -q matplotlib-venn\n",
        "\n",
        "# Now the newly-installed library can be used anywhere else in the notebook.\n",
        "from matplotlib_venn import venn2\n",
        "_ = venn2(subsets = (3, 2, 1))"
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7ff29f6e5f28>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "LxZ3dPzYnyNF",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Forms\n",
        "\n",
        "Forms can be used to parameterize code. See the [forms example notebook](/notebooks/forms.ipynb) for more details."
      ]
    },
    {
      "metadata": {
        "id": "FQ_Hx_9tn7uF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "93c0bca6-dc21-485f-db5c-f7c20f540223"
      },
      "cell_type": "code",
      "source": [
        "#@title Examples\n",
        "\n",
        "text = 'value' #@param \n",
        "date_input = '2018-03-22' #@param {type:\"date\"}\n",
        "number_slider = 0 #@param {type:\"slider\", min:-1, max:1, step:0.1}\n",
        "dropdown = '2nd option' #@param [\"1st option\", \"2nd option\", \"3rd option\"]\n"
      ],
      "execution_count": 65,
      "outputs": []
    },
    {
      "metadata": {
        "id": "-Rh3-Vt9Nev9",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# For more information:\n",
        "- [Overview of Colaboratory](/notebooks/basic_features_overview.ipynb)\n",
        "- [Importing libraries and installing dependencies](/notebooks/snippets/importing_libraries.ipynb)\n",
        "- [Markdown guide](/notebooks/markdown_guide.ipynb)\n",
        "- [Charts](/notebooks/charts.ipynb)\n",
        "- [Widgets](/notebooks/widgets.ipynb)\n",
        "- [Loading and saving data: local files, Drive, Sheets, Google Cloud Storage](/notebooks/io.ipynb)\n",
        "- [Example Google Cloud BigQuery notebook](/notebooks/bigquery.ipynb)\n",
        "- [TensorFlow with GPU](/notebooks/gpu.ipynb)\n",
        "- [Forms](/notebooks/forms.ipynb)"
      ]
    },
    {
      "metadata": {
        "id": "-drUlAGQNN_z",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "outputId": "8bd36a15-ec64-41c3-c1b6-eca74047b9e9"
      },
      "cell_type": "code",
      "source": [
        "!pip install plotly"
      ],
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: plotly in /usr/local/lib/python3.6/dist-packages (2.7.0)\r\n",
            "Requirement already satisfied: pytz in /usr/local/lib/python3.6/dist-packages (from plotly) (2018.4)\r\n",
            "Requirement already satisfied: nbformat>=4.2 in /usr/local/lib/python3.6/dist-packages (from plotly) (4.4.0)\r\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from plotly) (1.11.0)\r\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.6/dist-packages (from plotly) (2.18.4)\n",
            "Requirement already satisfied: decorator>=4.0.6 in /usr/local/lib/python3.6/dist-packages (from plotly) (4.3.0)\n",
            "Requirement already satisfied: traitlets>=4.1 in /usr/local/lib/python3.6/dist-packages (from nbformat>=4.2->plotly) (4.3.2)\n",
            "Requirement already satisfied: jsonschema!=2.5.0,>=2.4 in /usr/local/lib/python3.6/dist-packages (from nbformat>=4.2->plotly) (2.6.0)\n",
            "Requirement already satisfied: jupyter-core in /usr/local/lib/python3.6/dist-packages (from nbformat>=4.2->plotly) (4.4.0)\n",
            "Requirement already satisfied: ipython-genutils in /usr/local/lib/python3.6/dist-packages (from nbformat>=4.2->plotly) (0.2.0)\n",
            "Requirement already satisfied: idna<2.7,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests->plotly) (2.6)\n",
            "Requirement already satisfied: chardet<3.1.0,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests->plotly) (3.0.4)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests->plotly) (2018.4.16)\n",
            "Requirement already satisfied: urllib3<1.23,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests->plotly) (1.22)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "WI0M14VCOAaE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "7003d804-b9df-4b44-eb78-8192fbe59ac4"
      },
      "cell_type": "code",
      "source": [
        "import plotly.plotly as py\n",
        "import plotly.graph_objs as go\n",
        "%matplotlib inline\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import cufflinks as cf"
      ],
      "execution_count": 67,
      "outputs": []
    },
    {
      "metadata": {
        "id": "4XCh7wu4UriX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 360
        },
        "outputId": "8b82f960-908e-41cf-9e1a-39c4add405a1"
      },
      "cell_type": "code",
      "source": [
        "!pip install cufflinks"
      ],
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: cufflinks in /usr/local/lib/python3.6/dist-packages (0.12.1)\r\n",
            "Requirement already satisfied: plotly>=2.0.0 in /usr/local/lib/python3.6/dist-packages (from cufflinks) (2.7.0)\r\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.6/dist-packages (from cufflinks) (0.22.0)\r\n",
            "Requirement already satisfied: colorlover>=0.2 in /usr/local/lib/python3.6/dist-packages (from cufflinks) (0.2.1)\r\n",
            "Requirement already satisfied: pytz in /usr/local/lib/python3.6/dist-packages (from plotly>=2.0.0->cufflinks) (2018.4)\r\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.6/dist-packages (from plotly>=2.0.0->cufflinks) (2.18.4)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from plotly>=2.0.0->cufflinks) (1.11.0)\n",
            "Requirement already satisfied: nbformat>=4.2 in /usr/local/lib/python3.6/dist-packages (from plotly>=2.0.0->cufflinks) (4.4.0)\n",
            "Requirement already satisfied: decorator>=4.0.6 in /usr/local/lib/python3.6/dist-packages (from plotly>=2.0.0->cufflinks) (4.3.0)\n",
            "Requirement already satisfied: python-dateutil>=2 in /usr/local/lib/python3.6/dist-packages (from pandas->cufflinks) (2.5.3)\n",
            "Requirement already satisfied: numpy>=1.9.0 in /usr/local/lib/python3.6/dist-packages (from pandas->cufflinks) (1.14.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests->plotly>=2.0.0->cufflinks) (2018.4.16)\n",
            "Requirement already satisfied: chardet<3.1.0,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests->plotly>=2.0.0->cufflinks) (3.0.4)\n",
            "Requirement already satisfied: urllib3<1.23,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests->plotly>=2.0.0->cufflinks) (1.22)\n",
            "Requirement already satisfied: idna<2.7,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests->plotly>=2.0.0->cufflinks) (2.6)\n",
            "Requirement already satisfied: jsonschema!=2.5.0,>=2.4 in /usr/local/lib/python3.6/dist-packages (from nbformat>=4.2->plotly>=2.0.0->cufflinks) (2.6.0)\n",
            "Requirement already satisfied: ipython-genutils in /usr/local/lib/python3.6/dist-packages (from nbformat>=4.2->plotly>=2.0.0->cufflinks) (0.2.0)\n",
            "Requirement already satisfied: jupyter-core in /usr/local/lib/python3.6/dist-packages (from nbformat>=4.2->plotly>=2.0.0->cufflinks) (4.4.0)\n",
            "Requirement already satisfied: traitlets>=4.1 in /usr/local/lib/python3.6/dist-packages (from nbformat>=4.2->plotly>=2.0.0->cufflinks) (4.3.2)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "3rf83ZDBOenX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "4fff104d-63d9-4be1-be7d-8bb3a814491f"
      },
      "cell_type": "code",
      "source": [
        "from plotly.offline import download_plotlyjs,init_notebook_mode,plot,iplot"
      ],
      "execution_count": 69,
      "outputs": []
    },
    {
      "metadata": {
        "id": "c34aZkk_Oqmt",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "f6ffa383-5b9a-4ca6-e133-020a92c48533"
      },
      "cell_type": "code",
      "source": [
        "init_notebook_mode(connected=True)"
      ],
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window.Plotly) {{require(['plotly'],function(plotly) {window.Plotly=plotly;});}}</script>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "Yv--Gu0GOxlN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "1afc9d2c-b86b-4a5d-d92a-f94bf34e4c81"
      },
      "cell_type": "code",
      "source": [
        "data = dict (type='choropleth',locations=['AZ','CA','NY'],locationmode='USA-states',colorscale='Portland',text=['text 1','text 2','text 3'],z=[1.0,2.0,3.0],\n",
        "            colorbar={'title':\"Title Goes here\"})"
      ],
      "execution_count": 71,
      "outputs": []
    },
    {
      "metadata": {
        "id": "9h9WEsM3PrEc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        },
        "outputId": "7fe2106a-cb61-48e8-fd84-19d0d70759ea"
      },
      "cell_type": "code",
      "source": [
        "data"
      ],
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'colorbar': {'title': 'Title Goes here'},\n",
              " 'colorscale': 'Portland',\n",
              " 'locationmode': 'USA-states',\n",
              " 'locations': ['AZ', 'CA', 'NY'],\n",
              " 'text': ['text 1', 'text 2', 'text 3'],\n",
              " 'type': 'choropleth',\n",
              " 'z': [1.0, 2.0, 3.0]}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 72
        }
      ]
    },
    {
      "metadata": {
        "id": "npTf-XjePs4L",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "d8110903-7958-4211-c4d5-ab3e8878ab2c"
      },
      "cell_type": "code",
      "source": [
        "layout = dict (geo={'scope':'usa'})"
      ],
      "execution_count": 73,
      "outputs": []
    },
    {
      "metadata": {
        "id": "eHUQR5ZsP3x9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "7a2b40de-de6d-497e-aae4-528ef1ced443"
      },
      "cell_type": "code",
      "source": [
        "choromap= go.Figure(data=[data],layout=layout)"
      ],
      "execution_count": 74,
      "outputs": []
    },
    {
      "metadata": {
        "id": "8yj1BGNKRn_p",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "outputId": "3c6a5417-4581-420c-cc59-d2cf1c2d65e7"
      },
      "cell_type": "code",
      "source": [
        "iplot(choromap)"
      ],
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div id=\"e672d4e7-3391-4da7-9f4c-cb60b505b857\" style=\"height: 525px; width: 100%;\" class=\"plotly-graph-div\"></div><script type=\"text/javascript\">require([\"plotly\"], function(Plotly) { window.PLOTLYENV=window.PLOTLYENV || {};window.PLOTLYENV.BASE_URL=\"https://plot.ly\";Plotly.newPlot(\"e672d4e7-3391-4da7-9f4c-cb60b505b857\", [{\"type\": \"choropleth\", \"locations\": [\"AZ\", \"CA\", \"NY\"], \"locationmode\": \"USA-states\", \"colorscale\": \"Portland\", \"text\": [\"text 1\", \"text 2\", \"text 3\"], \"z\": [1.0, 2.0, 3.0], \"colorbar\": {\"title\": \"Title Goes here\"}}], {\"geo\": {\"scope\": \"usa\"}}, {\"showLink\": true, \"linkText\": \"Export to plot.ly\"})});</script>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "iaJDzyJNR-o_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "outputId": "f90dd98d-9699-46cf-b1a1-71837b033d6b"
      },
      "cell_type": "code",
      "source": [
        "iplot([{\"x\": [1, 2, 3], \"y\": [3, 1, 6]}])"
      ],
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div id=\"80f4d17a-ab01-445c-bd2e-89ed4727ce83\" style=\"height: 525px; width: 100%;\" class=\"plotly-graph-div\"></div><script type=\"text/javascript\">require([\"plotly\"], function(Plotly) { window.PLOTLYENV=window.PLOTLYENV || {};window.PLOTLYENV.BASE_URL=\"https://plot.ly\";Plotly.newPlot(\"80f4d17a-ab01-445c-bd2e-89ed4727ce83\", [{\"x\": [1, 2, 3], \"y\": [3, 1, 6]}], {}, {\"showLink\": true, \"linkText\": \"Export to plot.ly\"})});</script>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "GfVFAwyrQb8b",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "abe350b7-f191-4c4c-e013-718efb1be070"
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 49,
      "outputs": []
    },
    {
      "metadata": {
        "id": "h8Gzw_tuQe88",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}