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Each example assumes you’ve opened a database and clipped it to one evaluation: 
from pyfia import FIA, download

db = FIA(download("RI"))
db.clip_most_recent()        # use eval_type="GRM" for growth/mortality/removals

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Volume

from pyfia import volume, join_species_names

# Total net volume on forest land
total = volume(db, land_type="forest")
print(f"Total volume: {total['VOLCFNET_TOTAL'][0]:,.0f} cubic feet")

# By species, with common names
by_species = volume(db, by_species=True)
by_species = join_species_names(by_species, db)
print(by_species.sort("VOLCFNET_TOTAL", descending=True).head(10))

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Forest area by forest type

from pyfia import area

result = area(db, land_type="forest", grp_by="FORTYPCD")
# Output includes FORTYPCD and the auto-added FOREST_TYPE_GROUP name
print(result)

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Trees per acre by size class

from pyfia import tpa

result = tpa(db, land_type="forest", by_size_class=True)
print(result)

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Mortality

from pyfia import mortality

db.clip_most_recent(eval_type="GRM")   # GRM evaluation for mortality

# Annual mortality volume on timberland (growing stock)
mort = mortality(db, measure="volume", land_type="timber", tree_type="gs")
print(f"Annual mortality: {mort['MORT_TOTAL'][0]:,.0f} cu ft/year")

# By cause of death
by_agent = mortality(db, measure="volume", grp_by="AGENTCD")
print(by_agent)

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Growth and removals

from pyfia import growth, removals

db.clip_most_recent(eval_type="GRM")

grw = growth(db, measure="volume", land_type="forest")
rmv = removals(db, measure="volume", land_type="forest")

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Biomass and carbon

biomass() always returns both biomass and carbon columns — carbon is biomass × 0.47 (the standard IPCC fraction): 
from pyfia import biomass

result = biomass(db, component="AG", land_type="forest", totals=True)
print(f"Aboveground biomass: {result['BIO_TOTAL'][0]:,.0f} tons")
print(f"Carbon:              {result['CARB_TOTAL'][0]:,.0f} tons")
Components: "AG" (aboveground, default), "BG" (belowground), "TOTAL", "BOLE", "BRANCH", "FOLIAGE" (case-insensitive).

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Custom domain filtering

from pyfia import volume

# Pine sawtimber on gentle slopes
result = volume(
    db,
    land_type="timber",
    tree_domain="DIA >= 5.0 AND SPCD IN (110, 111, 121)",
    area_domain="SLOPE < 30",
)

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Working with multiple states

Downloading a list of states merges them into one database, so you can estimate across all of them and group by state: 
from pyfia import FIA, download, volume, join_state_names

db = FIA(download(["GA", "FL", "SC"]))
db.clip_most_recent()

by_state = volume(db, grp_by="STATECD")
print(join_state_names(by_state, db))

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Validate against EVALIDator

Check a pyFIA estimate against the official USFS tool: 
from pyfia import volume, validate_pyfia_estimate

vol = volume(db, tree_type="gs", vol_type="net")

result = validate_pyfia_estimate(
    vol,
    state_code=13,        # Georgia
    year=2023,
    estimate_type="volume",
)
print(f"Difference: {result.pct_diff:.2f}%")
See validate_pyfia_estimate() for the full signature.

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Runnable scripts

Complete, runnable scripts live in the examples/ directory of the repository — including harvest-panel analysis and mortality-by-cause. Clone and run: 
git clone https://github.com/mihiarc/pyfia.git
cd pyfia
uv run python examples/harvest_panel_analysis.py