leaf_engine.adapt.adapt_synthetic_rates

Functions

`calculate_reefer_rates`(→ pandas.DataFrame)	Calculates reefer rates.
`drop_duplicate_months`(date_cols)	Takes in a list of datetime values Returns a list with no more than one
`get_city_state_to_region_mappings`(→ pandas.DataFrame)	Returns the mappings for city+state->zip3->kma->region.
`get_rates_for_our_lanes`(_lanes, ...)
`get_region_to_region_reefer_multiples`()	Gets region-to-region multiples of reefer rates from DB.
`get_spot_rates`(→ pandas.DataFrame)
`get_synthetic_rates`(→ pandas.DataFrame)	This function uses DAT rate data to extrapolate spot rates for Leaf lanes.
`populate_dat_table_zip3`(→ pandas.DataFrame)	Adds origin and destination zip3 from reference city/state pairs.
`query_dat_table`(→ pandas.DataFrame)	Pulls synthetic rate data from the synspot schema.

leaf_engine.adapt.adapt_synthetic_rates.calculate_reefer_rates(dat_table: pandas.DataFrame) → pandas.DataFrame

Calculates reefer rates.

This uses a combination of dry rates, and region-to-region variances between dry and reefer.

leaf_engine.adapt.adapt_synthetic_rates.drop_duplicate_months(date_cols): Takes in a list of datetime values Returns a list with no more than one date per month per year.

leaf_engine.adapt.adapt_synthetic_rates.get_city_state_to_region_mappings() → pandas.DataFrame

Returns the mappings for city+state->zip3->kma->region.

leaf_engine.adapt.adapt_synthetic_rates.get_rates_for_our_lanes(_lanes, _dat_zip3_kma_all_rates, _dat_fuel)

leaf_engine.adapt.adapt_synthetic_rates.get_region_to_region_reefer_multiples(): Gets region-to-region multiples of reefer rates from DB.

leaf_engine.adapt.adapt_synthetic_rates.get_spot_rates(dat_table, _kma_markets, _dat_fuel) → pandas.DataFrame

leaf_engine.adapt.adapt_synthetic_rates.get_synthetic_rates(_dat_table: pandas.DataFrame, _kma_markets: pandas.DataFrame, _dat_fuel: float, _lanes: pandas.DataFrame) → pandas.DataFrame

This function uses DAT rate data to extrapolate spot rates for Leaf lanes.

Parameters:

Return type:

pandas.DataFrame

leaf_engine.adapt.adapt_synthetic_rates.populate_dat_table_zip3(dat_table: pandas.DataFrame) → pandas.DataFrame

Adds origin and destination zip3 from reference city/state pairs.

leaf_engine.adapt.adapt_synthetic_rates.query_dat_table() → pandas.DataFrame

Pulls synthetic rate data from the synspot schema.