Covid19_Dimensions_subset.ipynb

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        },
        "95b6d2637cbd4912933c5a0d74c2d4e5": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
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            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
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      }
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/alexwade/sandbox/blob/main/Covid19_Dimensions_subset.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Set up"
      ],
      "metadata": {
        "id": "J9u-lVm8d-Fa"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## authenticate"
      ],
      "metadata": {
        "id": "6P7C5ImeeBiQ"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E_vDPsnkNomz",
        "outputId": "bee55f42-a251-4ec3-914e-2bf3de69fb07"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Authenticated\n"
          ]
        }
      ],
      "source": [
        "from google.colab import auth\n",
        "auth.authenticate_user()\n",
        "print('Authenticated')\n",
        "\n",
        "import altair as alt"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "BDFN9tzkd8Yb"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## import the BigQuery library so that we can query into GBQ\n",
        "\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "-l_k6zDNdtoZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.cloud import bigquery\n",
        "client = bigquery.Client('test-project-291221')"
      ],
      "metadata": {
        "id": "sF4ADswYNtex"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from google.cloud.bigquery import magics\n",
        "magics.context.project = 'test-project-291221'\n",
        "project_id = 'test-project-291221'\n",
        "bq_params = {}\n",
        "\n",
        "client = bigquery.Client(project=project_id)\n",
        "%load_ext google.cloud.bigquery"
      ],
      "metadata": {
        "id": "iMSffWU8NumG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "54cd60b6-db1e-4e74-8ff1-abe6311eb0e4"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The google.cloud.bigquery extension is already loaded. To reload it, use:\n",
            "  %reload_ext google.cloud.bigquery\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Query the Covid19 dataset on GBQ"
      ],
      "metadata": {
        "id": "kwYdX-TVfO6v"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Get a list of doctypes and counts"
      ],
      "metadata": {
        "id": "f12F-6EWeWHm"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
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        },
        "id": "Ac2C-zSIcGOo",
        "outputId": "8bd9c413-9cf6-432b-aa75-4df9e05b1e47"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Query is running:   0%|          |"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d615c50d1cc74f0fb2ebdf4f63019069"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          |"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "252b3c81c6a2432e8204092263068980"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "%%bigquery bytype\n",
        "\n",
        "SELECT distinct(type), count(id) as count\n",
        "FROM `covid-19-dimensions-ai.data.publications`\n",
        "group by type order by count desc"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        " bytype"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "fIuJeaqdcv1d",
        "outputId": "b8f04937-7933-4bcc-f965-c11769929190"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         type    count\n",
              "0     article  2378842\n",
              "1     chapter   258003\n",
              "2    preprint   197069\n",
              "3  proceeding   117699\n",
              "4        book    47835\n",
              "5   monograph    26229\n",
              "6     seminar       33"
            ],
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              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
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              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
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              "      } catch (error) {\n",
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              "      }\n",
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              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
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              "      let quickchartButtonEl =\n",
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              "      quickchartButtonEl.style.display =\n",
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              "\n",
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              "    <style>\n",
              "      .colab-df-generate {\n",
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              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('bytype')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('bytype');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "bytype",
              "summary": "{\n  \"name\": \"bytype\",\n  \"rows\": 7,\n  \"fields\": [\n    {\n      \"column\": \"type\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"article\",\n          \"chapter\",\n          \"monograph\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"count\",\n      \"properties\": {\n        \"dtype\": \"Int64\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          2378842,\n          258003,\n          26229\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "alt.Chart(bytype).mark_bar().encode(\n",
        "    x='count',\n",
        "    y=alt.Y('type', sort='-x')\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 259
        },
        "id": "7oIqPbdIc9Yv",
        "outputId": "99fcdbbd-533f-4252-ba43-849ae66df69c"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/altair/utils/core.py:384: FutureWarning: the convert_dtype parameter is deprecated and will be removed in a future version.  Do ``ser.astype(object).apply()`` instead if you want ``convert_dtype=False``.\n",
            "  col = df[col_name].apply(to_list_if_array, convert_dtype=False)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
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              "\n",
              "<div id=\"altair-viz-b82f79d3939248bab4eacf53a68e585b\"></div>\n",
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              "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
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              "    function displayChart(vegaEmbed) {\n",
              "      vegaEmbed(outputDiv, spec, embedOpt)\n",
              "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
              "    }\n",
              "\n",
              "    if(typeof define === \"function\" && define.amd) {\n",
              "      requirejs.config({paths});\n",
              "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
              "    } else {\n",
              "      maybeLoadScript(\"vega\", \"5\")\n",
              "        .then(() => maybeLoadScript(\"vega-lite\", \"4.17.0\"))\n",
              "        .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
              "        .catch(showError)\n",
              "        .then(() => displayChart(vegaEmbed));\n",
              "    }\n",
              "  })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}}, \"data\": {\"name\": \"data-8d378c3c7794ca2a378c30980fc3f5bd\"}, \"mark\": \"bar\", \"encoding\": {\"x\": {\"field\": \"count\", \"type\": \"quantitative\"}, \"y\": {\"field\": \"type\", \"sort\": \"-x\", \"type\": \"nominal\"}}, \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.17.0.json\", \"datasets\": {\"data-8d378c3c7794ca2a378c30980fc3f5bd\": [{\"type\": \"article\", \"count\": 2378842}, {\"type\": \"chapter\", \"count\": 258003}, {\"type\": \"preprint\", \"count\": 197069}, {\"type\": \"proceeding\", \"count\": 117699}, {\"type\": \"book\", \"count\": 47835}, {\"type\": \"monograph\", \"count\": 26229}, {\"type\": \"seminar\", \"count\": 33}]}}, {\"mode\": \"vega-lite\"});\n",
              "</script>"
            ],
            "text/plain": [
              "alt.Chart(...)"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## What are the classification model outputs for type=proceeding?"
      ],
      "metadata": {
        "id": "dWmOnfWfcR61"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
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            "6019df39cc784a049bbace766002755e",
            "62983c2fe06e47698413679283596ac6",
            "cf05ce78f39a47cfa20cac0fa9d86ba9",
            "40c411bebc6d4b2699b84e878e48ef88",
            "54275abfef89440798a7990c420e6271",
            "60e9246b03f5408a90c9bd384ca6608f",
            "e54d99c84f244a6b93791571f05720f9",
            "aec66be8f64b40ceaabc62e97a75f48c",
            "e081af5e158147baab5cbea7de2104df",
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        },
        "outputId": "7dddb4ca-72de-493f-edb2-3b7ae00bcc56",
        "id": "oQut6qHddpS2"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Query is running:   0%|          |"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "37f7b334a9054c46a56845fbefa719d6"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          |"
            ],
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              "version_major": 2,
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              "model_id": "60e9246b03f5408a90c9bd384ca6608f"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "%%bigquery doctypes\n",
        "\n",
        "SELECT distinct(document_type.classification) doctype, count(id) as count\n",
        "FROM `covid-19-dimensions-ai.data.publications`\n",
        "where type = \"proceeding\"\n",
        "group by doctype order by count desc"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "doctypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "yLFGwWVNeI1x",
        "outputId": "3461ec96-3bb6-4855-b232-ada7469d628d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                    doctype   count\n",
              "0          CONFERENCE_PAPER  105717\n",
              "1       CONFERENCE_ABSTRACT    7863\n",
              "2  OTHER_CONFERENCE_CONTENT    3303\n",
              "3                      None     815\n",
              "4        CORRECTION_ERRATUM       1"
            ],
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              "                                                    [key], {});\n",
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              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
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              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
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              "  .colab-df-quickchart {\n",
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              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
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              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('doctypes')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_f7c95172-26a3-43e6-aa45-3a9b9c71deab button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('doctypes');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "doctypes",
              "summary": "{\n  \"name\": \"doctypes\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"doctype\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"CONFERENCE_ABSTRACT\",\n          \"CORRECTION_ERRATUM\",\n          \"CONFERENCE_PAPER\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"count\",\n      \"properties\": {\n        \"dtype\": \"Int64\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          7863,\n          1,\n          3303\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "alt.Chart(doctypes).mark_bar().encode(\n",
        "    x='count',\n",
        "    y=alt.Y('doctype', sort='-x')\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 219
        },
        "id": "6YitW9beef7U",
        "outputId": "ec560dc0-3e91-41e9-9bbd-08c093dc0c52"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/altair/utils/core.py:384: FutureWarning: the convert_dtype parameter is deprecated and will be removed in a future version.  Do ``ser.astype(object).apply()`` instead if you want ``convert_dtype=False``.\n",
            "  col = df[col_name].apply(to_list_if_array, convert_dtype=False)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "<div id=\"altair-viz-5ea97daa4c654842905933d954b968f8\"></div>\n",
              "<script type=\"text/javascript\">\n",
              "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
              "  (function(spec, embedOpt){\n",
              "    let outputDiv = document.currentScript.previousElementSibling;\n",
              "    if (outputDiv.id !== \"altair-viz-5ea97daa4c654842905933d954b968f8\") {\n",
              "      outputDiv = document.getElementById(\"altair-viz-5ea97daa4c654842905933d954b968f8\");\n",
              "    }\n",
              "    const paths = {\n",
              "      \"vega\": \"https://cdn.jsdelivr.net/npm//vega@5?noext\",\n",
              "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm//vega-lib?noext\",\n",
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              "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm//vega-embed@6?noext\",\n",
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              "\n",
              "    function maybeLoadScript(lib, version) {\n",
              "      var key = `${lib.replace(\"-\", \"\")}_version`;\n",
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              "        new Promise(function(resolve, reject) {\n",
              "          var s = document.createElement('script');\n",
              "          document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
              "          s.async = true;\n",
              "          s.onload = () => {\n",
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              "          };\n",
              "          s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
              "          s.src = paths[lib];\n",
              "        });\n",
              "    }\n",
              "\n",
              "    function showError(err) {\n",
              "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
              "      throw err;\n",
              "    }\n",
              "\n",
              "    function displayChart(vegaEmbed) {\n",
              "      vegaEmbed(outputDiv, spec, embedOpt)\n",
              "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
              "    }\n",
              "\n",
              "    if(typeof define === \"function\" && define.amd) {\n",
              "      requirejs.config({paths});\n",
              "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
              "    } else {\n",
              "      maybeLoadScript(\"vega\", \"5\")\n",
              "        .then(() => maybeLoadScript(\"vega-lite\", \"4.17.0\"))\n",
              "        .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
              "        .catch(showError)\n",
              "        .then(() => displayChart(vegaEmbed));\n",
              "    }\n",
              "  })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}}, \"data\": {\"name\": \"data-7564de86f1c288a077e43fcc035ba812\"}, \"mark\": \"bar\", \"encoding\": {\"x\": {\"field\": \"count\", \"type\": \"quantitative\"}, \"y\": {\"field\": \"doctype\", \"sort\": \"-x\", \"type\": \"nominal\"}}, \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.17.0.json\", \"datasets\": {\"data-7564de86f1c288a077e43fcc035ba812\": [{\"doctype\": \"CONFERENCE_PAPER\", \"count\": 105717}, {\"doctype\": \"CONFERENCE_ABSTRACT\", \"count\": 7863}, {\"doctype\": \"OTHER_CONFERENCE_CONTENT\", \"count\": 3303}, {\"doctype\": null, \"count\": 815}, {\"doctype\": \"CORRECTION_ERRATUM\", \"count\": 1}]}}, {\"mode\": \"vega-lite\"});\n",
              "</script>"
            ],
            "text/plain": [
              "alt.Chart(...)"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "hHIoJ3sjSPx9"
      }
    }
  ]
}