BNP Paribas data request
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from trase.tools import sps
categorical_columns = [
# "flow_id",
"year",
# "biome",
"country_of_destination",
"country_of_production",
"economic_bloc",
"exporter",
"exporter_group",
"importer",
"importer_group",
# "logistics_hub",
# "municipality_of_production",
"port_of_export",
# "slaughterhouse",
# "state_of_production",
# "biome_trase_id",
"country_of_destination_trase_id",
"country_of_production_trase_id",
"economic_bloc_trase_id",
"exporter_trase_id",
"exporter_group_trase_id",
"importer_trase_id",
"importer_group_trase_id",
# "logistics_hub_trase_id",
# "municipality_of_production_trase_id",
"port_of_export_trase_id",
# "slaughterhouse_trase_id",
# "state_of_production_trase_id",
"zero_deforestation_brazil_beef",
# "biome.1",
# "decision_tree",
"product_type",
# "trase_geocode",
"forest_500_beef",
]
summation_columns = [
"cattle_deforestation_5_year_total_exposure",
"co2_gross_emissions_cattle_deforestation_5_year_total_exposure",
"co2_net_emissions_cattle_deforestation_5_year_total_exposure",
"fob",
# "land_use",
"volume",
]
year = 2018
df = sps.concat(
[
sps.get_pandas_df_once(
f"brazil/beef/sei_pcs/v2.2.0/FULL_{year}.csv",
usecols=[*summation_columns, *categorical_columns],
na_filter=False,
)
for year in [2018, 2019, 2020]
]
)
df = df.astype({c: float for c in summation_columns})
df = sps.consolidate(df, summation_columns, categorical_columns)
df.to_csv("brazil_beef_national_embedded_2.2.0.csv", index=False)