pipeline.src.flows.ports

Functions

make_date(date_string)

make_lat_lon(lat_lon)

extract_unece_locations(csv_directory_path)

clean_unece(locations)

load_unece(locations)

flow_make_unece_ports(csv_directory_path)

extract_circabc_locations(csv_filepath)

clean_circabc(locations)

load_circabc(locations)

flow_make_circabc_ports(csv_filepath)

extract_unece_ports()

extract_circabc_ports()

merge_circabc_unece(circabc_ports, unece_ports)

combine_columns_into_value(ports)

clean_ports(→ pandas.DataFrame)

Rename ambiguous port names and change incorrect country codes

parse_ports_names(ports)

load_port_codes(ports)

flow_combine_circabc_unece_ports()

geocode_row(row)

geocode_row_google(row)

extract_port_codes()

extract_active_ports_locodes(→ set)

flag_active_ports(→ pandas.DataFrame)

geocode_ports(ports)

load_geocoded_ports(geocoded_ports)

flow_geocode_ports()

extract_local_ports(→ pandas.DataFrame)

compute_ports_zones(→ pandas.DataFrame)

Compute ports FAO areas, façades and departments.

clean_fao_areas(→ pandas.DataFrame)

Keep only the smallest FAO area(s) of each port.

transform_ports_open_data(→ pandas.DataFrame)

invalidate_cache()

load_ports(ports)

ports_flow([dataset_id, ports_resource_id, ...])

Module Contents

pipeline.src.flows.ports.make_date(date_string: str)[source]
pipeline.src.flows.ports.make_lat_lon(lat_lon: str)[source]
pipeline.src.flows.ports.extract_unece_locations(csv_directory_path)[source]
pipeline.src.flows.ports.clean_unece(locations)[source]
pipeline.src.flows.ports.load_unece(locations)[source]
pipeline.src.flows.ports.flow_make_unece_ports(csv_directory_path: str)[source]
pipeline.src.flows.ports.extract_circabc_locations(csv_filepath)[source]
pipeline.src.flows.ports.clean_circabc(locations)[source]
pipeline.src.flows.ports.load_circabc(locations)[source]
pipeline.src.flows.ports.flow_make_circabc_ports(csv_filepath: str)[source]
pipeline.src.flows.ports.extract_unece_ports()[source]
pipeline.src.flows.ports.extract_circabc_ports()[source]
pipeline.src.flows.ports.merge_circabc_unece(circabc_ports, unece_ports)[source]
pipeline.src.flows.ports.combine_columns_into_value(ports)[source]
pipeline.src.flows.ports.clean_ports(ports: pandas.DataFrame) → pandas.DataFrame[source]

Rename ambiguous port names and change incorrect country codes

pipeline.src.flows.ports.parse_ports_names(ports)[source]
pipeline.src.flows.ports.load_port_codes(ports)[source]
pipeline.src.flows.ports.flow_combine_circabc_unece_ports()[source]
pipeline.src.flows.ports.geocode_row(row)[source]
pipeline.src.flows.ports.geocode_row_google(row)[source]
pipeline.src.flows.ports.extract_port_codes()[source]
pipeline.src.flows.ports.extract_active_ports_locodes() → set[source]
pipeline.src.flows.ports.flag_active_ports(ports: pandas.DataFrame, active_ports_locodes: set) → pandas.DataFrame[source]
pipeline.src.flows.ports.geocode_ports(ports)[source]
pipeline.src.flows.ports.load_geocoded_ports(geocoded_ports)[source]
pipeline.src.flows.ports.flow_geocode_ports()[source]
pipeline.src.flows.ports.extract_local_ports() → pandas.DataFrame[source]
pipeline.src.flows.ports.compute_ports_zones(ports: pandas.DataFrame) → pandas.DataFrame[source]

Compute ports FAO areas, façades and departments.

pipeline.src.flows.ports.clean_fao_areas(ports: pandas.DataFrame) → pandas.DataFrame[source]

Keep only the smallest FAO area(s) of each port.

pipeline.src.flows.ports.transform_ports_open_data(ports: pandas.DataFrame) → pandas.DataFrame[source]
pipeline.src.flows.ports.invalidate_cache()[source]
pipeline.src.flows.ports.load_ports(ports)[source]
pipeline.src.flows.ports.ports_flow(dataset_id: str = PORTS_DATASET_ID, ports_resource_id: str = PORTS_CSV_RESOURCE_ID, ports_resource_title: str = PORTS_CSV_RESOURCE_TITLE, is_integration: bool = IS_INTEGRATION, extract_local_ports_fn: Callable = extract_local_ports, update_resource_fn: Callable = update_resource, invalidate_cache_fn: Callable = invalidate_cache)[source]