This script will read a list of language names, and then attempt to categorize them based on common linguistic classifications. Since “language units” can be quite broad, I’ll focus on what’s typically most useful for organization: Language Family, Script, and potentially Geographic Region (though region can be very fluid).

For simplicity and practicality, the script will:

1. Read Language Names: It’ll take a list of language names (you can expand this list).
2. Categorize by Script: It’ll try to identify the primary script used by the language (e.g., Latin, Cyrillic, Arabic). This is often the easiest and most visually distinct “unit” for organization.
3. Categorize by Family (Simplified): It’ll assign a very broad language family. This part is simplified because definitive language family classification is complex and often requires a deep linguistic knowledge base (or a very large, pre-defined mapping). For a script, we’ll use common, easily identifiable families.
4. Organize into a Dictionary Structure: The output will be a dictionary that logically groups languages.

The Python Script

Python

import collections

def organize_languages(language_list):
    """
    Organizes a list of language names into a hierarchical structure
    based on script and a simplified language family.

    Args:
        language_list (list): A list of language names (strings).

    Returns:
        dict: A dictionary representing the organized language units.
              Example structure:
              {
                  "Latin_Script": {
                      "Indo-European": ["English", "Spanish"],
                      "Afro-Asiatic": ["Hausa"]
                  },
                  "Cyrillic_Script": {
                      "Indo-European": ["Russian"],
                      "Turkic": ["Kazakh"]
                  },
                  ...
              }
    """

    # --- Simplified Mappings ---
    # These mappings are simplified for demonstration.
    # For a comprehensive solution, you'd need a much larger
    # and more robust dataset (e.g., ISO 639-3 or Ethnologue data).

    # Common scripts associated with languages
    script_mapping = {
        "English": "Latin_Script",
        "Spanish": "Latin_Script",
        "French": "Latin_Script",
        "German": "Latin_Script",
        "Italian": "Latin_Script",
        "Portuguese": "Latin_Script",
        "Dutch": "Latin_Script",
        "Polish": "Latin_Script",
        "Czech": "Latin_Script",
        "Swedish": "Latin_Script",
        "Norwegian": "Latin_Script",
        "Danish": "Latin_Script",
        "Finnish": "Latin_Script",
        "Hungarian": "Latin_Script",
        "Turkish": "Latin_Script",
        "Vietnamese": "Latin_Script",
        "Hausa": "Latin_Script",
        "Swahili": "Latin_Script",
        "Yoruba": "Latin_Script",
        "Zulu": "Latin_Script",

        "Russian": "Cyrillic_Script",
        "Ukrainian": "Cyrillic_Script",
        "Bulgarian": "Cyrillic_Script",
        "Serbian": "Cyrillic_Script",
        "Macedonian": "Cyrillic_Script",
        "Kazakh": "Cyrillic_Script", # Also written in Latin & Arabic
        "Kyrgyz": "Cyrillic_Script",
        "Mongolian": "Cyrillic_Script", # Also written in Traditional Mongolian script

        "Arabic": "Arabic_Script",
        "Farsi": "Arabic_Script", # Persian, mostly Arabic script
        "Urdu": "Arabic_Script",
        "Sindhi": "Arabic_Script",
        "Pashto": "Arabic_Script",
        "Kurdish": "Arabic_Script", # Also Latin script

        "Hebrew": "Hebrew_Script",

        "Greek": "Greek_Script",

        "Armenian": "Armenian_Script",

        "Georgian": "Georgian_Script",

        "Amharic": "Ethiopic_Script",
        "Tigrinya": "Ethiopic_Script",

        "Japanese": "Japanese_Scripts", # Hiragana, Katakana, Kanji
        "Korean": "Korean_Script", # Hangul
        "Chinese (Mandarin)": "Han_Script", # Simplified Chinese characters
        "Chinese (Cantonese)": "Han_Script", # Traditional Chinese characters

        "Devanagari": "Devanagari_Script", # Not a language, but a script often associated
        "Hindi": "Devanagari_Script",
        "Marathi": "Devanagari_Script",
        "Nepali": "Devanagari_Script",
        "Sanskrit": "Devanagari_Script",

        "Bengali": "Bengali_Script",
        "Tamil": "Tamil_Script",
        "Telugu": "Telugu_Script",
        "Kannada": "Kannada_Script",
        "Malayalam": "Malayalam_Script",

        "Thai": "Thai_Script",
        "Lao": "Lao_Script",
        "Khmer": "Khmer_Script", # Cambodian

        "Tibetan": "Tibetan_Script",
        "Burmese": "Myanmar_Script", # Also known as Burmese script
    }

    # Broad language family mapping
    family_mapping = {
        "English": "Indo-European",
        "Spanish": "Indo-European",
        "French": "Indo-European",
        "German": "Indo-European",
        "Italian": "Indo-European",
        "Portuguese": "Indo-European",
        "Dutch": "Indo-European",
        "Polish": "Indo-European",
        "Czech": "Indo-European",
        "Swedish": "Indo-European",
        "Norwegian": "Indo-European",
        "Danish": "Indo-European",
        "Russian": "Indo-European",
        "Ukrainian": "Indo-European",
        "Bulgarian": "Indo-European",
        "Serbian": "Indo-European",
        "Macedonian": "Indo-European",
        "Greek": "Indo-European",
        "Farsi": "Indo-European", # Persian
        "Urdu": "Indo-European",
        "Hindi": "Indo-European",
        "Marathi": "Indo-European",
        "Nepali": "Indo-European",
        "Sanskrit": "Indo-European",
        "Bengali": "Indo-European",

        "Arabic": "Afro-Asiatic",
        "Hebrew": "Afro-Asiatic",
        "Amharic": "Afro-Asiatic",
        "Tigrinya": "Afro-Asiatic",
        "Hausa": "Afro-Asiatic",

        "Finnish": "Uralic",
        "Hungarian": "Uralic",

        "Turkish": "Turkic",
        "Kazakh": "Turkic",
        "Kyrgyz": "Turkic",

        "Japanese": "Japonic",
        "Korean": "Koreanic", # Often considered a language isolate
        "Chinese (Mandarin)": "Sino-Tibetan",
        "Chinese (Cantonese)": "Sino-Tibetan",
        "Tibetan": "Sino-Tibetan",
        "Burmese": "Sino-Tibetan",
        "Thai": "Tai-Kadai",
        "Lao": "Tai-Kadai",
        "Vietnamese": "Austroasiatic",
        "Khmer": "Austroasiatic",

        "Swahili": "Niger-Congo",
        "Yoruba": "Niger-Congo",
        "Zulu": "Niger-Congo",

        "Armenian": "Indo-European", # Isolate branch
        "Georgian": "Kartvelian",

        "Pashto": "Indo-European",
        "Sindhi": "Indo-European",
        "Kurdish": "Indo-European",
        "Tamil": "Dravidian",
        "Telugu": "Dravidian",
        "Kannada": "Dravidian",
        "Malayalam": "Dravidian",
        "Mongolian": "Mongolic",
    }

    # Using defaultdict for easier nested dictionary creation
    organized_data = collections.defaultdict(lambda: collections.defaultdict(list))

    unknown_languages = []

    for lang in language_list:
        script = script_mapping.get(lang)
        family = family_mapping.get(lang)

        if script and family:
            organized_data[script][family].append(lang)
        elif script:
            # If we only know the script, put it in an "Unknown Family" category
            organized_data[script]["Unknown_Family"].append(lang)
        else:
            unknown_languages.append(lang)

    # Convert defaultdicts to regular dicts for cleaner output
    final_organized_data = {
        script: dict(families) for script, families in organized_data.items()
    }

    return final_organized_data, unknown_languages

def print_organized_languages(organized_data):
    """Prints the organized language data in a readable format."""
    print("--- Organized Languages ---")
    for script, families in organized_data.items():
        print(f"\n## {script.replace('_', ' ')}:")
        print("---")
        for family, languages in families.items():
            print(f"### {family.replace('_', ' ')}:")
            for lang in sorted(languages):
                print(f"- {lang}")

def main():
    # Example list of languages (you can expand this!)
    my_languages = [
        "English", "Spanish", "French", "German", "Russian", "Arabic",
        "Japanese", "Korean", "Chinese (Mandarin)", "Hindi", "Swahili",
        "Turkish", "Finnish", "Hungarian", "Hebrew", "Greek", "Armenian",
        "Georgian", "Amharic", "Vietnamese", "Thai", "Urdu", "Farsi",
        "Kazakh", "Mongolian", "Polish", "Italian", "Portuguese",
        "Dutch", "Czech", "Swedish", "Norwegian", "Danish", "Ukrainian",
        "Bulgarian", "Serbian", "Macedonian", "Kyrgyz", "Pashto",
        "Sindhi", "Kurdish", "Tamil", "Telugu", "Kannada", "Malayalam",
        "Yoruba", "Zulu", "Burmese", "Khmer", "Lao", "Sanskrit", "Marathi",
        "Nepali", "Devanagari" # Note: Devanagari is a script, not a language, included for demo
    ]

    organized_results, unassigned = organize_languages(my_languages)
    print_organized_languages(organized_results)

    if unassigned:
        print("\n--- Unassigned Languages (Missing mapping data) ---")
        for lang in sorted(unassigned):
            print(f"- {lang}")

if __name__ == "__main__":
    main()

---

How the Script Works and How to Extend It

1. organize_languages(language_list) Function:
   • script_mapping: This dictionary is the core for identifying the script. It maps language names to their primary script. You’ll notice I’ve included a note for languages that can use multiple scripts (like Kazakh) – the current mapping picks one.
   • family_mapping: This dictionary maps languages to their broad language families. This is a simplified linguistic classification.
   • collections.defaultdict: This handy Python feature makes it easy to build nested dictionaries without constantly checking if a key exists before adding to it.
   • Looping and Categorization: The script iterates through your language_list. For each language, it tries to find its script and family in the respective mappings. If found, it adds the language to the organized_data dictionary under script -> family.
   • unknown_languages: If a language isn’t found in either mapping, it’s added to this list so you know what needs further attention.
2. print_organized_languages(organized_data) Function:
   • This simply takes the structured dictionary and prints it in a human-readable format using markdown headings.
3. main() Function:
   • This is where you’d put your list of languages.
   • It calls organize_languages and then print_organized_languages to show the results.

Extending Your Language Units

This script provides a good starting point, but the world of languages is incredibly rich and complex! Here’s how you can extend this “language unit” organization:

• Expand Mappings: The script_mapping and family_mapping dictionaries are crucial.
  • For Scripts: Consult resources like Unicode’s script blocks or Wikipedia’s lists of writing systems. Be aware that many languages use more than one script (e.g., Serbian uses both Cyrillic and Latin, Hindi can be written in Devanagari or other scripts historically). You might want to categorize by primary script or allow for multiple script assignments.
  • For Families: Refer to linguistic databases like Ethnologue, Glottolog, or Wikipedia’s lists of language families. Language families are often nested (e.g., Indo-European -> Romance -> Western Romance -> French). You could add more levels to your dictionary structure if you want this granularity (e.g., organized_data[script][family][sub_family].append(lang)).
• Add More Units:
  • Geographic Region: You could add another layer to your dictionary, mapping languages to continents or specific regions (e.g., organized_data[script][family][region].append(lang)).
  • Dialect Clusters: For some languages, you might want to group by major dialect clusters (e.g., for Arabic, Egyptian Arabic, Levantine Arabic, etc.).
  • Official Status: You could include data on whether a language is official in certain countries.
• Use External Data: Instead of hardcoding dictionaries, you could:
  • CSV/JSON Files: Store your language data (name, script, family, region, etc.) in a structured file (CSV or JSON) and have the script read from there. This makes your data more manageable.
  • APIs: For very advanced scenarios, you might find APIs for linguistic databases, but this is usually overkill for a personal organization project.
• Handle Ambiguity:
  • Multiple Scripts/Families: Decide how you want to handle languages that belong to multiple categories. The current script picks one based on the mapping order. You could modify it to list a language under all applicable categories.

This script should give you a powerful foundation for organizing your language data!