Greek NT Noun Morphology — Case, Gender, Declension¶

Statistical profile of all noun tokens in the Greek New Testament. Data: MACULA Greek Nestle1904.

This notebook covers:

• Case, gender, and number distribution across the whole GNT
• Case × gender crosstab
• Article (ὁ/ἡ/τό) co-occurrence rates by case
• Top noun lemmas by frequency
• Genre comparison (Gospels & Acts / Pauline / General & Rev)

In [ ]:

import sys
sys.path.insert(0, '../../../src')

from bible_grammar.nt_noun_profile import (
    print_nt_noun_overview,
    print_nt_noun_case, print_nt_noun_gender,
    print_nt_noun_case_gender,
    print_nt_article_stats,
    print_nt_noun_top_lemmas,
    print_nt_noun_genre_profile,
    print_nt_noun_book_distribution,
    nt_noun_case_chart, nt_noun_gender_chart,
    nt_noun_case_gender_heatmap,
    nt_noun_genre_heatmap, nt_noun_book_chart,
    nt_noun_lemma_case,
)

import sys sys.path.insert(0, '../../../src') from bible_grammar.nt_noun_profile import ( print_nt_noun_overview, print_nt_noun_case, print_nt_noun_gender, print_nt_noun_case_gender, print_nt_article_stats, print_nt_noun_top_lemmas, print_nt_noun_genre_profile, print_nt_noun_book_distribution, nt_noun_case_chart, nt_noun_gender_chart, nt_noun_case_gender_heatmap, nt_noun_genre_heatmap, nt_noun_book_chart, nt_noun_lemma_case, )

---

1. Overview & Key Statistics¶

Greek nouns inflect for case, gender, and number.

• Case indicates syntactic function: nominative (subject), accusative (direct object), genitive (possession/relationship), dative (indirect object/instrument), vocative (address)
• Gender is grammatical, not biological: masculine, feminine, neuter
• Number: singular or plural (dual rare; absent in NT)

The definite article (ὁ/ἡ/τό) fully inflects for case, gender, and number — making it one of the most important forms to master in the early chapters of BBG.

In [ ]:

print_nt_noun_overview()

print_nt_noun_overview()

---

2. Case Distribution¶

The nominative and accusative together account for the majority of noun tokens — reflecting the SVO/VSO sentence structure of NT Greek. The genitive is the third most common case (possession, origin, partitive, subjective/objective uses). The dative is less frequent; the vocative rarest.

In [ ]:

print_nt_noun_case()          # whole GNT

# Compare narrative vs. epistolary
print_nt_noun_case('Jhn')     # John — high genitive (relational language)
print_nt_noun_case('Rom')     # Romans — argument-heavy; dative and accusative prominent

print_nt_noun_case() # whole GNT # Compare narrative vs. epistolary print_nt_noun_case('Jhn') # John — high genitive (relational language) print_nt_noun_case('Rom') # Romans — argument-heavy; dative and accusative prominent

In [ ]:

from IPython.display import Image
path = nt_noun_case_chart()
print(f'Saved: {path}')
Image(str(path))

from IPython.display import Image path = nt_noun_case_chart() print(f'Saved: {path}') Image(str(path))

---

3. Gender Distribution¶

Greek grammatical gender must be memorized with each noun, but patterns exist:

• 2nd declension -ος nouns are almost always masculine
• 1st declension -α/-η nouns are almost always feminine
• 2nd declension -ον nouns are always neuter
• 3rd declension varies — gender is part of the lexical entry

In [ ]:

print_nt_noun_gender()        # whole GNT

print_nt_noun_gender() # whole GNT

In [ ]:

from IPython.display import Image
path = nt_noun_gender_chart()
print(f'Saved: {path}')
Image(str(path))

from IPython.display import Image path = nt_noun_gender_chart() print(f'Saved: {path}') Image(str(path))

---

4. Case × Gender Crosstab¶

This crosstab shows which case–gender combinations are most frequent — the forms a student will encounter most often when reading the GNT.

In [ ]:

print_nt_noun_case_gender()   # whole GNT

print_nt_noun_case_gender() # whole GNT

In [ ]:

from IPython.display import Image
path = nt_noun_case_gender_heatmap()
print(f'Saved: {path}')
Image(str(path))

from IPython.display import Image path = nt_noun_case_gender_heatmap() print(f'Saved: {path}') Image(str(path))

---

5. Article Co-occurrence by Case¶

The Greek definite article is anaphoric (refers back to known referents) and nominalizing (turns any word into a noun phrase). Not every noun takes an article:

• Proper names often appear without the article
• Predicate nouns in equative clauses are typically anarthrous (Colwell's Rule)
• Abstract nouns may appear either way

This table shows what percentage of nouns in each case appear with the preceding article.

In [ ]:

print_nt_article_stats()      # whole GNT

# Compare by book
print_nt_article_stats('Jhn') # John uses article heavily
print_nt_article_stats('Rev') # Revelation — often anarthrous for stylistic emphasis

print_nt_article_stats() # whole GNT # Compare by book print_nt_article_stats('Jhn') # John uses article heavily print_nt_article_stats('Rev') # Revelation — often anarthrous for stylistic emphasis

---

6. Most Frequent Noun Lemmas¶

Knowing the top 30 noun lemmas gives a student access to the most common nominal concepts in the GNT: θεός, κύριος, Ἰησοῦς, Χριστός, ἄνθρωπος, πατήρ, etc.

In [ ]:

print_nt_noun_top_lemmas(30)

print_nt_noun_top_lemmas(30)

In [ ]:

# Case distribution for the top 10 nouns
ct = nt_noun_lemma_case(top_n=10)
ct

# Case distribution for the top 10 nouns ct = nt_noun_lemma_case(top_n=10) ct

---

7. Genre Comparison — Case % by Book Group¶

Genre affects noun case distribution:

• Narratives (Gospels/Acts): high nominative (subjects of verbs) and accusative (objects)
• Epistles (Pauline): more genitive (relational constructs, possession)
• Revelation: distinctive case patterns from Hebraistic Greek style

In [ ]:

print_nt_noun_genre_profile()

print_nt_noun_genre_profile()

In [ ]:

from IPython.display import Image
path = nt_noun_genre_heatmap()
print(f'Saved: {path}')
Image(str(path))

from IPython.display import Image path = nt_noun_genre_heatmap() print(f'Saved: {path}') Image(str(path))

---

8. Distribution Across NT Books¶

In [ ]:

print_nt_noun_book_distribution()

print_nt_noun_book_distribution()

In [ ]:

from IPython.display import Image
path = nt_noun_book_chart()
print(f'Saved: {path}')
Image(str(path))

from IPython.display import Image path = nt_noun_book_chart() print(f'Saved: {path}') Image(str(path))

---

Quick Reference¶

from bible_grammar.nt_noun_profile import (
    # Data
    nt_noun_data,                # all GNT noun tokens (DataFrame)
    nt_noun_case_profile,        # case distribution
    nt_noun_gender_profile,      # gender distribution
    nt_noun_number_profile,      # number distribution
    nt_noun_case_gender,         # case × gender crosstab
    nt_noun_top_lemmas,          # top noun lemmas by frequency
    nt_noun_lemma_case,          # lemma × case crosstab
    nt_noun_book_distribution,   # counts per NT book
    nt_noun_genre_profile,       # case % by genre group
    nt_article_stats,            # article vs anarthrous by case
    # Print
    print_nt_noun_overview,
    print_nt_noun_case,          # accepts book= kwarg
    print_nt_noun_gender,
    print_nt_noun_case_gender,
    print_nt_article_stats,
    print_nt_noun_top_lemmas,
    print_nt_noun_genre_profile,
    print_nt_noun_book_distribution,
    # Charts
    nt_noun_case_chart,
    nt_noun_gender_chart,
    nt_noun_case_gender_heatmap,
    nt_noun_genre_heatmap,
    nt_noun_book_chart,
)