Greek NT Verb Morphology — Tense, Voice, Mood¶

Statistical profile of all verb tokens in the Greek New Testament. Data: MACULA Greek Nestle1904 (~37,000 GNT verb tokens).

This notebook covers:

• Tense, voice, and mood distribution across the whole GNT
• Tense × voice and tense × mood crosstabs
• Top verb lemmas by frequency
• Distribution across NT books
• Genre comparison (Gospels & Acts / Pauline / General & Rev)

In [ ]:

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

from bible_grammar.nt_verb_profile import (
    print_nt_verb_overview,
    print_nt_verb_tense, print_nt_verb_voice, print_nt_verb_mood,
    print_nt_verb_tense_voice,
    print_nt_verb_top_lemmas,
    print_nt_verb_genre_profile,
    print_nt_verb_book_distribution,
    nt_verb_tense_chart, nt_verb_voice_chart, nt_verb_mood_chart,
    nt_verb_tense_voice_heatmap,
    nt_verb_genre_heatmap, nt_verb_book_chart,
    nt_verb_tense_profile, nt_verb_mood_profile,
    nt_verb_lemma_tense,
)

import sys sys.path.insert(0, '../../../src') from bible_grammar.nt_verb_profile import ( print_nt_verb_overview, print_nt_verb_tense, print_nt_verb_voice, print_nt_verb_mood, print_nt_verb_tense_voice, print_nt_verb_top_lemmas, print_nt_verb_genre_profile, print_nt_verb_book_distribution, nt_verb_tense_chart, nt_verb_voice_chart, nt_verb_mood_chart, nt_verb_tense_voice_heatmap, nt_verb_genre_heatmap, nt_verb_book_chart, nt_verb_tense_profile, nt_verb_mood_profile, nt_verb_lemma_tense, )

---

1. Overview & Key Statistics¶

The GNT contains roughly 37,000 finite and non-finite verb tokens. Three dimensions of Greek verb morphology matter most for reading:

• Tense (present, imperfect, future, aorist, perfect, pluperfect) — encodes aspect and, in the indicative, time reference
• Voice (active, middle, passive) — encodes the relationship of subject to action
• Mood (indicative, subjunctive, optative, imperative, infinitive, participle) — encodes the speaker's attitude toward the reality of the action

In [ ]:

print_nt_verb_overview()

print_nt_verb_overview()

---

2. Tense Distribution¶

The aorist is the single most common tense in the GNT — the default form for narrating completed events. The present dominates discourse and teaching (Epistles, direct speech). The perfect is rarer but theologically dense (γέγραπται, πεπίστευκα).

In [ ]:

print_nt_verb_tense()         # whole GNT

# Book-level comparison
print_nt_verb_tense('Mat')    # narrative-heavy Gospel
print_nt_verb_tense('Rom')    # argument-heavy Epistle

print_nt_verb_tense() # whole GNT # Book-level comparison print_nt_verb_tense('Mat') # narrative-heavy Gospel print_nt_verb_tense('Rom') # argument-heavy Epistle

In [ ]:

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

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

---

3. Voice Distribution¶

Greek has three voices:

• Active — subject performs the action directly
• Middle — subject performs the action with reflexive interest (often identical form to passive in aorist/future)
• Passive — subject receives the action; the passive participle γέγραπται ('it is written') is among the most theologically significant forms in the GNT

In [ ]:

print_nt_verb_voice()          # whole GNT
print_nt_verb_voice('Rev')     # apocalyptic — high passive ('divine passive') rate

print_nt_verb_voice() # whole GNT print_nt_verb_voice('Rev') # apocalyptic — high passive ('divine passive') rate

In [ ]:

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

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

---

4. Mood Distribution¶

The indicative dominates (straightforward assertion). The participle and infinitive are non-finite forms that function as verbal adjectives and nouns. The subjunctive signals purpose, condition, or indirect command; the imperative commands directly; the optative is rare (Pauline prayers, μὴ γένοιτο — 'May it never be!').

In [ ]:

print_nt_verb_mood()           # whole GNT
print_nt_verb_mood('Luk')      # Luke-Acts: narrative participles are extremely common
print_nt_verb_mood('1Co')      # Pauline: imperative and subjunctive clusters

print_nt_verb_mood() # whole GNT print_nt_verb_mood('Luk') # Luke-Acts: narrative participles are extremely common print_nt_verb_mood('1Co') # Pauline: imperative and subjunctive clusters

In [ ]:

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

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

---

5. Tense × Voice Crosstab¶

Some tense–voice combinations are extremely common (aorist active, present active); others are rare (perfect passive, pluperfect middle). This crosstab reveals the morphological "terrain" a student will encounter.

In [ ]:

print_nt_verb_tense_voice()    # whole GNT

print_nt_verb_tense_voice() # whole GNT

In [ ]:

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

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

---

6. Most Frequent Verb Lemmas¶

The top GNT verbs by token count. εἰμί (to be), λέγω (to say), and ἔχω (to have) consistently rank highest. Knowing these 30 lemmas means recognising a significant fraction of all verb tokens in the GNT.

In [ ]:

print_nt_verb_top_lemmas(30)

print_nt_verb_top_lemmas(30)

In [ ]:

# Lemma × tense crosstab for the top 12 verbs
ct = nt_verb_lemma_tense(top_n=12)
ct

# Lemma × tense crosstab for the top 12 verbs ct = nt_verb_lemma_tense(top_n=12) ct

---

7. Distribution Across NT Books¶

Verb density (% of book words) varies by genre. Narrative books (Luke, Acts, John) have high verb counts; shorter epistles (Philemon, 3 John) are low in absolute tokens but may have comparable verb density.

In [ ]:

print_nt_verb_book_distribution()

print_nt_verb_book_distribution()

In [ ]:

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

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

---

8. Genre Comparison — Tense % by Book Group¶

Genre shapes verb morphology strongly:

• Gospels & Acts: narrative → heavy aorist, past indicative, participle chains
• Pauline letters: argument → present/aorist balance, subjunctive (purpose clauses), imperative clusters
• General & Rev: varies from wisdom exhortation (James) to apocalyptic vision (Revelation)

In [ ]:

print_nt_verb_genre_profile()

print_nt_verb_genre_profile()

In [ ]:

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

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

---

Quick Reference¶

from bible_grammar.nt_verb_profile import (
    # Data
    nt_verb_data,               # all GNT verb tokens (DataFrame)
    nt_verb_tense_profile,      # tense distribution
    nt_verb_voice_profile,      # voice distribution
    nt_verb_mood_profile,       # mood distribution
    nt_verb_tense_voice,        # tense × voice crosstab
    nt_verb_tense_mood,         # tense × mood crosstab
    nt_verb_top_lemmas,         # top verb lemmas by frequency
    nt_verb_lemma_tense,        # lemma × tense crosstab
    nt_verb_book_distribution,  # counts per NT book
    nt_verb_genre_profile,      # tense % by genre group
    # Print
    print_nt_verb_overview,
    print_nt_verb_tense,        # accepts book= kwarg
    print_nt_verb_voice,
    print_nt_verb_mood,
    print_nt_verb_tense_voice,
    print_nt_verb_top_lemmas,
    print_nt_verb_genre_profile,
    print_nt_verb_book_distribution,
    # Charts
    nt_verb_tense_chart,
    nt_verb_voice_chart,
    nt_verb_mood_chart,
    nt_verb_tense_voice_heatmap,
    nt_verb_genre_heatmap,
    nt_verb_book_chart,
)