def assign_naics(df):
cw_load = load_bea_crosswalk()
cw = cw_load[['BEA_2012_Detail_Code', 'NAICS_2012_Code']].drop_duplicates().reset_index(drop=True)
# drop all rows with naics >6
cw = cw[cw['NAICS_2012_Code'].apply(lambda x: len(str(x)) == 6)].reset_index(drop=True)
df = pd.merge(df, cw, left_on='Activity', right_on='BEA_2012_Detail_Code')
df = df.drop(columns=["BEA_2012_Detail_Code"])
df = df.rename(columns={"NAICS_2012_Code": "Sector"})
df['SectorSourceName'] = 'NAICS_2012_Code'
return df
if __name__ == '__main__':
# select years to pull unique activity names
years = ['2002']
# df of unique ers activity names
df = unique_activity_names('BEA_Make_Table', years)
# add manual naics 2012 assignments
df = assign_naics(df)
# drop any rows where naics12 is 'nan' (because level of detail not needed or to prevent double counting)
df.dropna(subset=["Sector"], inplace=True)
# assign sector type
df['SectorType'] = None
# sort df
df = order_crosswalk(df)
# save as csv
df.to_csv(datapath + "activitytosectormapping/" + "Crosswalk_BEA_Make_Table_toNAICS.csv", index=False)
return df
if __name__ == '__main__':
# select years to pull unique activity names
year = '2002'
# datasource
datasource = 'Blackhurst_IO'
# df of unique ers activity names
df = unique_activity_names(datasource, year)
# add manual naics 2012 assignments
df = assign_naics(df)
# drop any rows where naics12 is 'nan'
# (because level of detail not needed or to prevent double counting)
df.dropna(subset=["Sector"], inplace=True)
# assign sector type
df['SectorType'] = None
# subset to just the sectors used in water allocation m2. Must reexamine
# crosswalk for additional BEA activities to ensure no data loss and
# accurate mapping
sector_list = ['21', '54136']
df2 = df.loc[df['Sector'].str.startswith(
tuple(sector_list))].reset_index(drop=True)
# sort df
df2 = order_crosswalk(df2)
# save as csv
df.to_csv(datapath + "activitytosectormapping/" + "NAICS_Crosswalk_" +
datasource + ".csv",
index=False)
