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Development","sub_category":"Taxonomy and Ontology","monthly_downloads":0,"total_dependent_repos":0,"total_dependent_packages":0,"readme":"\n# Taxonomy4Good\n\u003cbr/\u003e\u003cbr/\u003e\n\u003cp align=\"center\"\u003e\n    \u003ca href=\"https://good-data-hub.gitbook.io/gdh-github-landing-page/\" target=\"_blank\"\u003e\n       \u003cimg  src=\"./images/gooddatahub-logo.png\" alt=\"Good Data Hub\" width=\"60%\"/\u003e\n    \u003c/a\u003e\n\u003c/p\u003e\n\u003cbr/\u003e\u003cbr/\u003e\nGood Data Hub empowers impact-driven data scientists with simple tools that provide the highest quality of data and reporting. \n\u003cbr/\u003e\u003cbr/\u003e\nAnalysis of unstructured sustainability data is arduous, time-consuming, and expensive. \nOur goal is to reduce the barriers to accessing, processing, and analyzing sustainability data by providing an open-source sustainability lexicon. We are committed to developing tools that enhance the efficiency and practicality of working with such data. \n\n \u003cbr/\u003e\u003cbr/\u003e\nTaxonomy4good is the first open-source library for ESG and Sustainability standards and taxonomies. \n\n## Organizations that trust us\n\n\u003cdiv align=\"center\"\u003e\n    \u003ca href=\"https://businessacceleration.exeter.ac.uk/#home\" target=\"_blank\"\u003e\n        \u003cimg src=\"images/setsqrd.jpeg\" width=\"400\" /\u003e\n    \u003c/a\u003e\n    \u003ca href=\"https://www.exeter.ac.uk/\" target=\"_blank\"\u003e\n        \u003cimg src=\"images/exeter_uni.png\" width=\"400\" /\u003e\n    \u003c/a\u003e\n\u003c/div\u003e\n\n\n## What are Taxonomies?\nTaxonomy is the practice and science of categorization or classification. A taxonomy (or taxonomical classification) is a scheme of classification, specifically a hierarchical categorization and organization of data into distinct classes or groups based on shared characteristics.\n\n## Taxonomy4Good\nTaxonomy4good is the first and only centralized repository for Sustainability and ESG standards in code form, ready for data labeling and for use with an API to query relevant data. These data structures can also be leveraged in ML and NLP for ESG/Sustainability reporting and data processing. Users can seamlessly integrate the provided taxonomies into their workflow, or create a custom taxonomy to form a reporting structure for existing sustainability scoring models. \n\n### Use Cases\n \n1. Use with an API\n2. Data Tagging\n3. ML and Topic Modeling \n4. Supervised aspect based sentiment analysis \n5. Text classification \n6. Keyword extraction\n\n\n## Installation\n\n```\npip install taxonomy4good\n```\n\n## Quick Tour\n\n### Use existing taxonomy\nTo use an existing taxonomy, e.g. `ftse_fsgi`, you can import it directly as follows.\n```python\nfrom taxonomy4good import from_file\nftse_builtin_taxonomy = from_file(\"ftse_fsgi\")\n```\nAvailable Taxonomies:\n\n| Name                  | Description                                    |\n|-----------------------|------------------------------------------------|\n| `un_sdg_taxonomy`     | UN Sustainabile Development Goals              |\n| `eu_taxonomy`         | European Union Taxonomy                        |\n| `ftse_fsgi`           | FTSE for Social Good Index                     |\n| `world_bank_taxonomy` | World Bank taxonomy                            |\n| `china_taxonomy`      | China Taxonomy                                 |\n| `esg_taxonomy`        | ESG standard taxonomy                          |\n| `en_master_lexicon`   | Structure of the entire sustainability lexicon |\n\n\n### Create custom taxonomy\nCreate a custom taxonomy from scratch using `SustainabilityItem` objects, then initialize one of the items \nas a root item to a newly created `SustainabilityTaxonomy`.\n```python\nfrom taxonomy4good import SustainabilityTaxonomy, SustainabilityItem\n\nroot = SustainabilityItem(id=0, name=\"New Taxonomy\")\nitem1 = SustainabilityItem(id=1, name=\"item1\", parent=root)\nitem2 = SustainabilityItem(id=2, name=\"item2\", parent=root)\nitem3 = SustainabilityItem(id=3, name=\"item3\", parent=item1)\nitem4 = SustainabilityItem(id=4, name=\"item4\", parent=item1)\nitem5 = SustainabilityItem(id=5, name=\"item5\", parent=item2)\nitem6 = SustainabilityItem(id=6, name=\"item6\", parent=item2)\nroot.children = [item1, item2]\nitem1.children = [item3, item4]\nitem2.children = [item5, item6]\n\ncustom_taxonomy = SustainabilityTaxonomy(root, version_name=\"Custom Taxonomy\")\n\ncustom_taxonomy.print_hierarchy()\n```\nSee the resulting taxonomy as follows.\n```\n\u003e\u003e\u003e custom_taxonomy.print_hierarchy()\nNew Taxonomy : 0\n│\n│\n├─────item1 : 0\n│       └───── item3 : 0\n│       └───── item4 : 0\n└─────item2 : 0\n        └───── item5 : 0\n        └───── item6 : 0\n```\n### Get all items and terms\nTo get all the items and terms of the taxonomy use the following lines.\n```python\n# list of all SustainabilityItem objects\nall_items = custom_taxonomy.get_items()\n\n# list of terms (item names)\nall_terms = custom_taxonomy.get_terms()\n```\nThe resulting terms are shown in the following snippet.\n```\n\u003e\u003e\u003e print(all_terms)\n['New Taxonomy', 'item1', 'item2', 'item3', 'item4']\n```\n### Search terms\nSearch for terms by providing a substring. This can help get relevant terms from `en_full_taxonomy`, \nproviding you with the most similar sustainability terms that will help query textual data from various APIs and extend \nML and NLP tasks.\n```python\nsearch_result = custom_taxonomy.search_items_by_name(\"item\")\nresulting_terms = [result.name for result in search_result]\n```\nThe resulting terms are:\n```\n\u003e\u003e\u003e print(resulting_terms)\n['item1', 'item2', 'item3', 'item4', 'item5', 'item6']\n```\n### Update and compute scores\nScores and weights can be updated using an external API or imported from an Excel sheet with the taxonomy.\nThe following is an alternative way to update the scores programmatically\n```python\n# update scores and weights\n# scores and weights can be updated using an API or from Excel\nall_items[3].score = 10\nall_items[3].weight = 0.3\nall_items[4].score = 23\nall_items[4].weight = 0.7\nall_items[5].score = 7.4\nall_items[5].weight = 0.5\nall_items[6].score = -13\nall_items[6].weight = 0.5\n\n# compute score\nroot_score = custom_taxonomy.compute_scores()\n```\nGet the result of the updates in the following snippet.\n```\n\u003e\u003e\u003e print(root_score)\n\n16.299999999999997\n\n\u003e\u003e\u003e custom_taxonomy.print_hierarchy()\n\nNew Taxonomy : 16.299999999999997\n│\n│\n├─────item1 : 19.099999999999998\n│       └───── item3 : 10\n│       └───── item4 : 23\n└─────item2 : -2.8\n        └───── item5 : 7.4\n        └───── item6 : -13\n```\n### Finding children\n```python\nroot_children = all_items[0].children\nroot_children_names = [child.name for child in root_children]\n```\n\n```\n\u003e\u003e\u003e print(root_children_names)\n['item1', 'item2']\n```\n### Who is the parent\n```python\nitem_parent = all_items[1].parent\n```\n\n```\n\u003e\u003e\u003e print(item_parent.name)\nNew Taxonomy\n```\n\n### Import your own taxonomy\nCeate your own taxonomy in Excel and make use of the provided data structure `SustainabilityTaxonomy`.\nThe items of this data structure must include the following columns (attributes): `id`,`name`,`level`, `grouping`,\n `parent`,`score`, `weight`,`children`. Any other columns will be aggregated inside a dictionary called `meta_data`.\\\nFeel free to enrich your taxonomy with additional attributes!\\\nThe following is an example Excel file that is filled manually to provide a custom taxonomy.\n\u003cp align=\"center\"\u003e\n   \u003cimg src=\"./images/taxonomy_example.png\" alt=\"Taxonomy Example\" width=\"70%\"/\u003e\n\u003c/p\u003e\n\nThe columns `Acronym`, `Col 1`, and `Col 2` will be included in the attribute `meta_data` of the resulting \n`SustainabilityTaxonomy` object, as shown below.\n\n```python\nfrom taxonomy4good import from_file\n\nexample = from_file(\"examples/taxonomy example.xlsx\", filetype=\"excel\", meta=True)\n```\nThe resulting taxonomy can be printed as follows.\n```\n\u003e\u003e\u003e example.print_hierarchy()\nStandard Taxonomy : 0\n│\n│\n├─────Environment : 0\n│       └───── Air quality : 0\n│              └───── Air pollution : 0\n│              └───── Ozone layer : 0\n│       └───── Climate impacts : 0\n│              └───── United Nations Climate Change Conference : 0\n│              └───── Climate Change : 0\n│              └───── Sustainability Accounting Standards Board : 0\n│              └───── COP26 : 0\n│       └───── Ecosystem Impacts : 0\n│              └───── Flood Damage : 0\n│              └───── Ecosystem Conservation : 0\n└─────Social : 0\n        └───── Product Quality and Safety : 0\n               └───── Access/Affordability : 0\n               └───── Product Recall : 0\n               └───── Quality Control : 0\n               └───── Product Safety : 0\n               └───── Customer Satisfaction : 0\n        └───── Stakeholder relations : 0\n               └───── Charity : 0\n               └───── Donations : 0\n               └───── Community Outreach : 0\n```\nTo check the attributes of an item search for the item by `id` or by `name` as follows.\n```python\nsocial_item = example.search_items_by_name(\"Social\")[0]\n```\nor\n```python\nsocial_item = example.search_by_id(13)[0]\n```\nPrinting the details of a certain `SustainabilityItem` object works as follows.\n```\n\u003e\u003e\u003e social_item.details()\nname: Social\nid: 13\nlevel: 1\nchildren: [14, 20]\nparent: 0\nscore: 0\nweight: 1\nmeta_data: {'Acronym': None, 'Col 1': None, 'Col 2': None}\n```\nNote how `meta_data` stored the additional columns introduced in the Excel file.\n## Overview of all functions\n\n| Function                                             | Description                                                                                 |\n|------------------------------------------------------|---------------------------------------------------------------------------------------------|\n| `insert_items(items)`                                | Insert additional items (terms/lexicons) to this existing taxonomy                          |\n| `remove_subtree(items)`                              | Remove the passed items along with their children from the taxonomy                         |\n| `remove_by_id(ids)`                                  | Remove from the taxonomy items corresponding to the supplied ids                            |\n| `get_items_each_level(start_root)`                   | Get lists of items for each level of the taxonomy (grouped by level)                        |\n| `get_level_items(level)`                             | Get items of the specified level                                                            |\n| `get_items(start_root)`                              | Get all the items of the structure                                                          |\n| `get_terms(start_root)`                              | Get all terms (names/lexicon) in the taxonomy                                               |\n| `get_all_ids(start_root)`                            | Get ids of all the nodes in the current taxonomy (grouped by level)                         |\n| `search_by_id(ids)`                                  | Search for items by their id                                                                |\n| `level(start_item)`                                  | Compute the maximum depth/level of the taxonomy                                             |\n| `to_csv(filepath, start_root)`                       | Save current taxonomy/substructure to a csv file                                            |\n| `to_excel(filepath, start_root)`                     | Save current taxonomy/substructure to an Excel file                                         |\n| `items_to_json(filepath, start_root)`                | Save current taxonomy/substructure items to a JSON file (records structure)                 |\n| `taxonomy_to_json(filepath, start_root)`             | Save current taxonomy/substructure items to a JSON file (hierarchical structure)            |\n| `print_hierarchy(start_item, current_level, islast)` | Print the current hierarchy of the taxonomy with the respective values                      |\n| `get_level_scores(level)`                            | Compute the weighted values/scores for the specified level                                  |\n| `compute_scores(start_root, root_score)`             | Compute the weighted scores for the entire taxonomy                                         |\n| `summary()`                                          | Print the general information about the entire taxonomy                                     |\n| `to_dataframe(start_root)`                           | Convert the entire taxonomy to a DataFrame                                                  |\n| `similar_items(sustainability_items)`                | Gives the items under the same parent                                                       |\n| `similar_items_byid(ids)`                            | Gives the items under the same parent as items having the specified ids                     |\n| `search_items_by_name(terms, start_root)`            | Look for similar SustainabilityItems using a string partial match                           |\n| `search_similar_names(terms, start_root)`            | Search for similar names/terms in the taxonomy using a string partial match                 |\n| `items_to_dict(start_root)`                          | Convert the entire taxonomy to a list of dictionaries (records) starting from start_root    |\n| `taxonomy_to_dict(start_root)`                       | Convert the entire taxonomy to a dictionary (structural hierarchy) starting from start_root |\n## Community\nJoin Good Data Hub's community of sustainability data scientists and ML enthusiasts. \n\n🌟 Leave a star on our [GitHub](https://github.com/GoodDataHub/taxonomy4good).\n\n🐞 Report bugs with [GitHub Issues](https://github.com/GoodDataHub/taxonomy4good/issues).\n\n🔗 Follow us on [Linkedin](https://www.linkedin.com/company/81945779/admin/feed/posts/).\n","funding_links":[],"readme_doi_urls":[],"works":{},"citation_counts":{},"total_citations":0,"keywords_from_contributors":[],"project_url":"https://ost.ecosyste.ms/api/v1/projects/314160","html_url":"https://ost.ecosyste.ms/projects/314160"}