taxonomy4good 2.0.0

Sustainability lexicon providing both listed and non-listed taxonomies

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.

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.

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]

custom_taxonomy = SustainabilityTaxonomy(root, version_name="Custom Taxonomy")

custom_taxonomy.print_hierarchy()

See the resulting taxonomy as follows.

>>> custom_taxonomy.print_hierarchy()
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.

# list of all SustainabilityItem objects
all_items = custom_taxonomy.get_items()

# list of terms (item names)
all_terms = custom_taxonomy.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.

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

# update scores and weights
# scores and weights can be updated using an API or from Excel
all_items[3].score = 10
all_items[3].weight = 0.3
all_items[4].score = 23
all_items[4].weight = 0.7
all_items[5].score = 7.4
all_items[5].weight = 0.5
all_items[6].score = -13
all_items[6].weight = 0.5

# compute score
root_score = custom_taxonomy.compute_scores()

Get the result of the updates in the following snippet.

>>> print(root_score)

16.299999999999997

>>> custom_taxonomy.print_hierarchy()

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

Finding children

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

>>> print(root_children_names)
['item1', 'item2']

Who is the parent

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.

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.print_hierarchy()
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.

social_item = example.search_items_by_name("Social")[0]

or

social_item = example.search_by_id(13)[0]

Printing the details of a certain SustainabilityItem object works as follows.

>>> social_item.details()
name: Social
id: 13
level: 1
children: [14, 20]
parent: 0
score: 0
weight: 1
meta_data: {'Acronym': None, 'Col 1': None, 'Col 2': None}

Note how meta_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 start_root
taxonomy_to_dict(start_root)	Convert the entire taxonomy to a dictionary (structural hierarchy) starting from start_root

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