Taxonomy4Good

Good Data Hub empowers impact-driven data scientists with simple tools that provide the highest quality of data and reporting.

Analysis of unstructured sustainability data is arduous, time-consuming, and expensive. Our 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.

Taxonomy4good is the first open-source library for ESG and Sustainability standards and taxonomies.

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What are Taxonomies?

Taxonomy 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.

Taxonomy4Good

Taxonomy4good 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.

Use Cases

1. Use with an API
2. Data Tagging
3. ML and Topic Modeling
4. Supervised aspect based sentiment analysis
5. Text classification
6. Keyword extraction

Installation

pip install taxonomy4good

Quick Tour

Use existing taxonomy

To use an existing taxonomy, e.g. ftse_fsgi, you can import it directly as follows. python from taxonomy4good import from_file ftse_builtin_taxonomy = from_file("ftse_fsgi") Available Taxonomies:

| Name | Description | |-----------------------|------------------------------------------------| | un_sdg_taxonomy | UN Sustainabile Development Goals | | eu_taxonomy | European Union Taxonomy | | ftse_fsgi | FTSE for Social Good Index | | world_bank_taxonomy | World Bank taxonomy | | china_taxonomy | China Taxonomy | | esg_taxonomy | ESG standard taxonomy | | en_master_lexicon | Structure of the entire sustainability lexicon |

Create custom taxonomy

Create a custom taxonomy from scratch using SustainabilityItem objects, then initialize one of the items as a root item to a newly created SustainabilityTaxonomy. ```python from taxonomy4good import SustainabilityTaxonomy, SustainabilityItem

root = SustainabilityItem(id=0, name="New Taxonomy") item1 = SustainabilityItem(id=1, name="item1", parent=root) item2 = SustainabilityItem(id=2, name="item2", parent=root) item3 = SustainabilityItem(id=3, name="item3", parent=item1) item4 = SustainabilityItem(id=4, name="item4", parent=item1) item5 = SustainabilityItem(id=5, name="item5", parent=item2) item6 = SustainabilityItem(id=6, name="item6", parent=item2) root.children = [item1, item2] item1.children = [item3, item4] item2.children = [item5, item6]

customtaxonomy = SustainabilityTaxonomy(root, versionname="Custom Taxonomy")

customtaxonomy.printhierarchy() See the resulting taxonomy as follows.

customtaxonomy.printhierarchy() New Taxonomy : 0 │ │ ├─────item1 : 0 │ └───── item3 : 0 │ └───── item4 : 0 └─────item2 : 0 └───── item5 : 0 └───── item6 : 0 ```

Get all items and terms

To get all the items and terms of the taxonomy use the following lines. ```python

list of all SustainabilityItem objects

allitems = customtaxonomy.get_items()

list of terms (item names)

allterms = customtaxonomy.get_terms() The resulting terms are shown in the following snippet.

print(all_terms) ['New Taxonomy', 'item1', 'item2', 'item3', 'item4'] ```

Search terms

Search for terms by providing a substring. This can help get relevant terms from en_full_taxonomy, providing you with the most similar sustainability terms that will help query textual data from various APIs and extend ML and NLP tasks. python search_result = custom_taxonomy.search_items_by_name("item") resulting_terms = [result.name for result in search_result] The resulting terms are: print(resulting_terms) ['item1', 'item2', 'item3', 'item4', 'item5', 'item6']

Update and compute scores

Scores and weights can be updated using an external API or imported from an Excel sheet with the taxonomy. The following is an alternative way to update the scores programmatically ```python

update scores and weights

scores and weights can be updated using an API or from Excel

allitems[3].score = 10 allitems[3].weight = 0.3 allitems[4].score = 23 allitems[4].weight = 0.7 allitems[5].score = 7.4 allitems[5].weight = 0.5 allitems[6].score = -13 allitems[6].weight = 0.5

compute score

rootscore = customtaxonomy.compute_scores() Get the result of the updates in the following snippet.

print(root_score)

16.299999999999997

customtaxonomy.printhierarchy()

New Taxonomy : 16.299999999999997 │ │ ├─────item1 : 19.099999999999998 │ └───── item3 : 10 │ └───── item4 : 23 └─────item2 : -2.8 └───── item5 : 7.4 └───── item6 : -13 ```

Finding children

python root_children = all_items[0].children root_children_names = [child.name for child in root_children]

```

print(rootchildrennames) ['item1', 'item2'] ```

Who is the parent

python item_parent = all_items[1].parent

```

print(item_parent.name) New Taxonomy ```

Import your own taxonomy

Ceate your own taxonomy in Excel and make use of the provided data structure SustainabilityTaxonomy. The items of this data structure must include the following columns (attributes): id,name,level, grouping, parent,score, weight,children. Any other columns will be aggregated inside a dictionary called meta_data.\ Feel free to enrich your taxonomy with additional attributes!\ The following is an example Excel file that is filled manually to provide a custom taxonomy.

The columns Acronym, Col 1, and Col 2 will be included in the attribute meta_data of the resulting SustainabilityTaxonomy object, as shown below.

```python from taxonomy4good import from_file

example = from_file("examples/taxonomy example.xlsx", filetype="excel", meta=True) The resulting taxonomy can be printed as follows.

example.printhierarchy() Standard Taxonomy : 0 │ │ ├─────Environment : 0 │ └───── Air quality : 0 │ └───── Air pollution : 0 │ └───── Ozone layer : 0 │ └───── Climate impacts : 0 │ └───── United Nations Climate Change Conference : 0 │ └───── Climate Change : 0 │ └───── Sustainability Accounting Standards Board : 0 │ └───── COP26 : 0 │ └───── Ecosystem Impacts : 0 │ └───── Flood Damage : 0 │ └───── Ecosystem Conservation : 0 └─────Social : 0 └───── Product Quality and Safety : 0 └───── Access/Affordability : 0 └───── Product Recall : 0 └───── Quality Control : 0 └───── Product Safety : 0 └───── Customer Satisfaction : 0 └───── Stakeholder relations : 0 └───── Charity : 0 └───── Donations : 0 └───── Community Outreach : 0 To check the attributes of an item search for the item by `id` or by `name` as follows. python socialitem = example.searchitemsbyname("Social")[0] or python socialitem = example.searchbyid(13)[0] Printing the details of a certain `SustainabilityItem` object works as follows. socialitem.details() name: Social id: 13 level: 1 children: [14, 20] parent: 0 score: 0 weight: 1 metadata: {'Acronym': None, 'Col 1': None, 'Col 2': None} `` Note howmeta_data` stored the additional columns introduced in the Excel file.

Overview of all functions

| Function | Description | |------------------------------------------------------|---------------------------------------------------------------------------------------------| | insert_items(items) | Insert additional items (terms/lexicons) to this existing taxonomy | | remove_subtree(items) | Remove the passed items along with their children from the taxonomy | | remove_by_id(ids) | Remove from the taxonomy items corresponding to the supplied ids | | get_items_each_level(start_root) | Get lists of items for each level of the taxonomy (grouped by level) | | get_level_items(level) | Get items of the specified level | | get_items(start_root) | Get all the items of the structure | | get_terms(start_root) | Get all terms (names/lexicon) in the taxonomy | | get_all_ids(start_root) | Get ids of all the nodes in the current taxonomy (grouped by level) | | search_by_id(ids) | Search for items by their id | | level(start_item) | Compute the maximum depth/level of the taxonomy | | to_csv(filepath, start_root) | Save current taxonomy/substructure to a csv file | | to_excel(filepath, start_root) | Save current taxonomy/substructure to an Excel file | | items_to_json(filepath, start_root) | Save current taxonomy/substructure items to a JSON file (records structure) | | taxonomy_to_json(filepath, start_root) | Save current taxonomy/substructure items to a JSON file (hierarchical structure) | | print_hierarchy(start_item, current_level, islast) | Print the current hierarchy of the taxonomy with the respective values | | get_level_scores(level) | Compute the weighted values/scores for the specified level | | compute_scores(start_root, root_score) | Compute the weighted scores for the entire taxonomy | | summary() | Print the general information about the entire taxonomy | | to_dataframe(start_root) | Convert the entire taxonomy to a DataFrame | | similar_items(sustainability_items) | Gives the items under the same parent | | similar_items_byid(ids) | Gives the items under the same parent as items having the specified ids | | search_items_by_name(terms, start_root) | Look for similar SustainabilityItems using a string partial match | | search_similar_names(terms, start_root) | Search for similar names/terms in the taxonomy using a string partial match | | items_to_dict(start_root) | Convert the entire taxonomy to a list of dictionaries (records) starting from startroot | | `taxonomytodict(startroot)` | Convert the entire taxonomy to a dictionary (structural hierarchy) starting from start_root |

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