LOD_SPARQL/01_BNB_SPARQL_Compare_Publication_Year_for_two_Subjects.ipynb

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      "cell_type": "markdown",
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      "source": [
        "<div class=\"jumbotron jumbotron-fluid\">\n",
        "  <div class=\"container\">\n",
        "    <h1 class=\"display-4\">BNB: SPARQL query<br>Compare the # of resources <br>published by year for two Subjects</h1>\n",
        "    <p class=\"lead\">This notebook explains how to query it to retrieve records of resources indexed under two given Subjects / Topics (LCSH), and compare their number by publication year.</p>\n",
        "  </div>\n",
        "</div>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "view-in-github"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/BL-Labs/Jupyter-notebooks-projects-using-BL-Sources/blob/master/LOD_SPARQL/01_BNB_SPARQL_Compare_Publication_Year_for_two_Subjects.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
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    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "TU0Elp2wIoK_"
      },
      "source": [
        "The [BNB Linked Data Platform](https://bnb.data.bl.uk/) provides access to the [British National Bibliography (BNB)](http://www.bl.uk/bibliographic/natbib.html) published as linked open data and made available through SPARQL services. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "rkWxy-LgIoLB"
      },
      "source": [
        "<p class=\"alert alert-warning\" role=\"alert\">\n",
        "\n",
        "## Define the Subjects to Compare\n",
        "\n",
        "Set the Subject to search from [LCSH list](http://id.loc.gov/authorities/subjects.html):"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "jbYUkXcXIoLC",
        "trusted": false,
        "colab": {}
      },
      "source": [
        "# Examples -- uncomment the desired subject, or added your own set of Subject and Label, and do \"Run\" or \"Runtime\" > \"Run all\" or \"Run all cells\":\n",
        "\n",
        "Label = ''\n",
        "\n",
        "# Subject = 'Nanotubes'\n",
        "# Label = 'Nanotubes'\n",
        "\n",
        "Subject1 = 'Climaticchanges'\n",
        "Label1 = 'Climatic Changes'\n",
        "\n",
        "Subject2 = 'Globalwarming'\n",
        "Label2 = 'Global Warming'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "qt0a7zDeIoLH"
      },
      "source": [
        "#### Required modules / libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "grVj5kG8IoLH",
        "trusted": true,
        "colab": {}
      },
      "source": [
        "import requests\n",
        "import pandas as pd\n",
        "import json\n",
        "import csv\n",
        "import matplotlib.pyplot as plt\n",
        "from pandas import json_normalize"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "OGC6e5K8IoLL"
      },
      "source": [
        "## Let's query the repository by asking the publications indexed by the defined Subjects\n",
        "We will use the [SPARQL endpoint](https://bnb.data.bl.uk/flint-sparql) to create the query and configure the request to retrieve json as a result."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "orhVTXt0IoLL",
        "trusted": false,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "5fe9cbee-7b1f-4c98-bcf3-f7084761c33c"
      },
      "source": [
        "url = 'https://bnb.data.bl.uk/sparql'\n",
        "query = \"\"\"\n",
        "PREFIX bibo: <http://purl.org/ontology/bibo/> \n",
        "PREFIX dct: <http://purl.org/dc/terms/> \n",
        "PREFIX schema: <http://schema.org/> \n",
        "PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#> \n",
        "\n",
        "SELECT ?resource ?isbn ?title ?date ?author ?authorUri WHERE {{\n",
        "      ?resource dct:subject <http://bnb.data.bl.uk/id/concept/lcsh/{0}>;  \n",
        "            dct:title ?title ; \n",
        "            schema:author ?authorUri ; \n",
        "            schema:datePublished ?date . \n",
        "      ?authorUri rdfs:label ?author .\n",
        "}}  \n",
        "\"\"\"\n",
        "\n",
        "# Query for Subject 1\n",
        "query1 = query.format(Subject1)\n",
        "\n",
        "# use json as a result\n",
        "headers = {'Accept': 'application/sparql-results+json'}\n",
        "r = requests.get(url, params = {'format': 'application/sparql-results+json', 'query': query1}, headers=headers)\n",
        "print('Records retrieved for subject ' + Label1)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Records retrieved for subject Climatic Changes\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "_LQpszOt0vZw",
        "trusted": false,
        "colab": {}
      },
      "source": [
        "# print(r.text)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "IPV7YE5QIoLS"
      },
      "source": [
        "## Load the data into DataFrames"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "0G0_FjkEIoLT",
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        },
        "outputId": "f9669dbb-9346-497d-8a79-e15c6660ae2d"
      },
      "source": [
        "bnbdata = json.loads(r.text)\n",
        "dfSub1 = json_normalize(bnbdata['results']['bindings']) \n",
        "dfSub1.head(2)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>resource.type</th>\n",
              "      <th>resource.value</th>\n",
              "      <th>title.type</th>\n",
              "      <th>title.value</th>\n",
              "      <th>date.type</th>\n",
              "      <th>date.value</th>\n",
              "      <th>author.type</th>\n",
              "      <th>author.value</th>\n",
              "      <th>authorUri.type</th>\n",
              "      <th>authorUri.value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>uri</td>\n",
              "      <td>http://bnb.data.bl.uk/id/resource/019483741</td>\n",
              "      <td>literal</td>\n",
              "      <td>Dwelling in the age of climate change : the et...</td>\n",
              "      <td>literal</td>\n",
              "      <td>2019-08</td>\n",
              "      <td>literal</td>\n",
              "      <td>Kelly, Elaine</td>\n",
              "      <td>uri</td>\n",
              "      <td>http://bnb.data.bl.uk/id/person/KellyElaine</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>uri</td>\n",
              "      <td>http://bnb.data.bl.uk/id/resource/019483741</td>\n",
              "      <td>literal</td>\n",
              "      <td>Dwelling in the age of climate change : the et...</td>\n",
              "      <td>literal</td>\n",
              "      <td>2019-08</td>\n",
              "      <td>literal</td>\n",
              "      <td>Kelly, Elaine</td>\n",
              "      <td>uri</td>\n",
              "      <td>http://bnb.data.bl.uk/id/person/KellyElaine</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  resource.type  ...                              authorUri.value\n",
              "0           uri  ...  http://bnb.data.bl.uk/id/person/KellyElaine\n",
              "1           uri  ...  http://bnb.data.bl.uk/id/person/KellyElaine\n",
              "\n",
              "[2 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "WMtokbR5Ew9j",
        "trusted": false,
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          "base_uri": "https://localhost:8080/",
          "height": 318
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        "outputId": "25ccb99b-694b-4483-8e76-f17a709a5d03"
      },
      "source": [
        "# Query for Subject 2\n",
        "query2 = query.format(Subject2)\n",
        "\n",
        "r = requests.get(url, params = {'format': 'application/sparql-results+json', 'query': query2}, headers=headers)\n",
        "print('Elements retrieved for subject ' + Label2)\n",
        "\n",
        "bnbdata = json.loads(r.text)\n",
        "dfSub2 = json_normalize(bnbdata['results']['bindings']) \n",
        "dfSub2.head(2)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Elements retrieved for subject Global Warming\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
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              "    .dataframe tbody tr th {\n",
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              "    .dataframe thead th {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>resource.type</th>\n",
              "      <th>resource.value</th>\n",
              "      <th>title.type</th>\n",
              "      <th>title.value</th>\n",
              "      <th>date.type</th>\n",
              "      <th>date.value</th>\n",
              "      <th>author.type</th>\n",
              "      <th>author.value</th>\n",
              "      <th>authorUri.type</th>\n",
              "      <th>authorUri.value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>uri</td>\n",
              "      <td>http://bnb.data.bl.uk/id/resource/018543304</td>\n",
              "      <td>literal</td>\n",
              "      <td>Global climate change : turning knowledge into...</td>\n",
              "      <td>literal</td>\n",
              "      <td>2017-09</td>\n",
              "      <td>literal</td>\n",
              "      <td>Kitchen, David, 1956-</td>\n",
              "      <td>uri</td>\n",
              "      <td>http://bnb.data.bl.uk/id/person/KitchenDavid1956-</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>uri</td>\n",
              "      <td>http://bnb.data.bl.uk/id/resource/018543304</td>\n",
              "      <td>literal</td>\n",
              "      <td>Global climate change : turning knowledge into...</td>\n",
              "      <td>literal</td>\n",
              "      <td>2017-09</td>\n",
              "      <td>literal</td>\n",
              "      <td>Kitchen, David, 1956-</td>\n",
              "      <td>uri</td>\n",
              "      <td>http://bnb.data.bl.uk/id/person/KitchenDavid1956-</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  resource.type  ...                                    authorUri.value\n",
              "0           uri  ...  http://bnb.data.bl.uk/id/person/KitchenDavid1956-\n",
              "1           uri  ...  http://bnb.data.bl.uk/id/person/KitchenDavid1956-\n",
              "\n",
              "[2 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "DMgoGtlyIoLj"
      },
      "source": [
        "## How many items?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "bDsScd1gIoLk",
        "trusted": false,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "4bb9bfc3-6978-42ea-f042-367ec6e9a771"
      },
      "source": [
        "# How many items for each Subject?\n",
        "print('Number of records retrieved for subject ' + Label1 + ': ' + str(len(dfSub1)))\n",
        "print('Number of records retrieved for subject ' + Label2 + ': ' + str(len(dfSub2)))\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Number of records retrieved for subject Climatic Changes: 1211\n",
            "Number of records retrieved for subject Global Warming: 455\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "DXFWETl7IoLn"
      },
      "source": [
        "### Let's count the number of Resources by author"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "PjY-cqnqIoLo",
        "trusted": false,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "outputId": "acb353e7-181e-4fdd-c3d4-25e283e8fb2e"
      },
      "source": [
        "#Subject 1\n",
        "resources_by_author_Sub1 = dfSub1['author.value'].value_counts()\n",
        "print('Rank of Authors about ' + Label1)\n",
        "resources_by_author_Sub1"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Rank of Authors about Climatic Changes\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dessler, Andrew Emory                     12\n",
              "Houghton, J. T. (John Theodore), 1931-    12\n",
              "Gribbin, John, 1946-                      12\n",
              "Goodell, Jeff                             10\n",
              "Grove, Jean M.                            10\n",
              "                                          ..\n",
              "Goodess, C. M.                             1\n",
              "Jarman, Melanie                            1\n",
              "Craven, Greg                               1\n",
              "Bows, Alice, 1974-                         1\n",
              "Money, D. C.                               1\n",
              "Name: author.value, Length: 596, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "lQs08YqyTeva",
        "trusted": false,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "outputId": "1a6568e2-cef4-4a68-b07b-25d5c9225be1"
      },
      "source": [
        "#Subject 2\n",
        "resources_by_author_Sub2 = dfSub2['author.value'].value_counts()\n",
        "print('Rank of Authors about ' + Label2)\n",
        "resources_by_author_Sub2"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Rank of Authors about Global Warming\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Houghton, J. T. (John Theodore), 1931-       18\n",
              "Hansen, James E. (James Edward), 1941-       12\n",
              "Johansen, Bruce E. (Bruce Elliott), 1950-    10\n",
              "Booker, Christopher                           8\n",
              "Lynas, Mark, 1973-                            8\n",
              "                                             ..\n",
              "Gray, Vincent, Ph. D.                         1\n",
              "Nisbet, E. G. (Euan George)                   1\n",
              "Craven, Greg                                  1\n",
              "Jarman, Melanie                               1\n",
              "Ying, Jun                                     1\n",
              "Name: author.value, Length: 218, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "5ty7tAEtIoL9"
      },
      "source": [
        "### Create a chart to visualize the results\n",
        "First let's see a top of publication Dates:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "bzuMZwCSIoMH"
      },
      "source": [
        "### Let's group the books by year"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "D56P3lFPIoMJ",
        "trusted": false,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        },
        "outputId": "efac1f07-fa29-41da-913d-97e13d7f8c5a"
      },
      "source": [
        "# First we create a new column in pandas with the year\n",
        "dfSub1['year'] = pd.DatetimeIndex(dfSub1['date.value']).year\n",
        "dfSub2['year'] = pd.DatetimeIndex(dfSub2['date.value']).year\n",
        "\n",
        "# Check first five from Subject 1 books\n",
        "dfSub1['year'].head(5)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    2019\n",
              "1    2019\n",
              "2    2019\n",
              "3    2019\n",
              "4    2015\n",
              "Name: year, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "kWtHX4lZIoMc"
      },
      "source": [
        "### Creating the chart of resources per year"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "xgowNiOHJruE",
        "trusted": false,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 765
        },
        "outputId": "8ac838c9-a63d-43c6-8b20-0d4dd285b23c"
      },
      "source": [
        "# .sort_index() is very important = sorts by year, instead of the the regular count number\n",
        "\n",
        "resources_by_year_Sub1 = dfSub1['year'].value_counts().sort_index()\n",
        "resources_by_year_Sub2 = dfSub2['year'].value_counts().sort_index()\n",
        "\n",
        "# let's check for Subject 1\n",
        "resources_by_year_Sub1"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1974      1\n",
              "1975      2\n",
              "1976      4\n",
              "1977      2\n",
              "1978      4\n",
              "1979      3\n",
              "1981      4\n",
              "1982      4\n",
              "1983      2\n",
              "1984      1\n",
              "1986      2\n",
              "1988      3\n",
              "1989      8\n",
              "1990      4\n",
              "1992     12\n",
              "1993      5\n",
              "1994      2\n",
              "1995      9\n",
              "1996      4\n",
              "1997     14\n",
              "1998      7\n",
              "1999      8\n",
              "2000     15\n",
              "2001     15\n",
              "2002     12\n",
              "2003     18\n",
              "2004     16\n",
              "2005     17\n",
              "2006     18\n",
              "2007     34\n",
              "2008     51\n",
              "2009     44\n",
              "2010     56\n",
              "2011     39\n",
              "2012     42\n",
              "2013     50\n",
              "2014     42\n",
              "2015    108\n",
              "2016    115\n",
              "2017    118\n",
              "2018    125\n",
              "2019    116\n",
              "2020     55\n",
              "Name: year, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "fbVlWepbZzPJ",
        "trusted": false,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 459
        },
        "outputId": "fdd1e159-50ce-4926-c82b-fe2bd4bde378"
      },
      "source": [
        "plt.figure(figsize=(15,7))\n",
        "resources_by_year_Sub1.plot(marker='o', markerfacecolor='blue', markersize=1, color='skyblue', linewidth=2, label=Label1)\n",
        "resources_by_year_Sub2.plot(marker='x', color='orange', markersize=2, linewidth=2, label=Label2)\n",
        "plt.legend(title = 'Number of Resources by Publication Year', title_fontsize = '14')\n",
        "plt.xlabel(\"Year\")\n",
        "plt.ylabel(\"Number of Published Resources\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Number of Published Resources')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}