passengersim.database.

common_queries

Functions

bid_price_history(db, scenario[, ...])

Compute average bid price history over all legs for each carrier.

bookings_by_timeframe(db, scenario[, ...])

Average bookings and revenue by carrier, booking class, and timeframe.

carrier_history(db, *[, scenario, burn_samples])

Sample-level details of carrier-level measures.

demand_to_come(db, *[, scenario, burn_samples])

Demand by market and timeframe across each sample.

demand_to_come_summary(db, scenario[, ...])

Demand by market and timeframe across each sample.

displacement_history(db, scenario[, ...])

Compute average displacement cost history over all legs for each carrier.

edgar(db, *[, scenario, burn_samples])

Forecast accuracy information.

fare_class_mix(db, scenario[, burn_samples])

Fare class mix by carrier.

leg_detail(db, scenario[, burn_samples])

Dump leg_detail into a dataframe

leg_forecast_trace(db[, scenario, ...])

Recorded forecast of demand by leg.

leg_forecasts(db, *[, scenario, burn_samples])

Average forecasts of demand by leg, bucket, and days to departure.

leg_local_and_flow_by_class(db, scenario[, ...])

leg_sales_trace(db[, scenario, ...])

Recorded forecast of demand by leg.

load_factor_distribution(db, scenario[, ...])

load_factors(db, scenario[, burn_samples])

local_and_flow_yields(db, *[, scenario, ...])

Compute yields for local (nonstop) and flow (connecting) passengers.

od_fare_class_mix(db, orig, dest, scenario)

Fare class mix by carrier for a particular origin-destination market.

path_forecasts(db, *[, scenario, burn_samples])

Average forecasts of demand by path, class, and days to departure.

total_demand(db, scenario[, burn_samples])

Average total demand.