import util as ut mDataKey = '1RNcXXvO5d8JFmPCEn_u7Qy30MCsg4kKUBMyjYW-swUk' sheets = {'treedata':350897684, 'sitedescrip':0, 'fuels': 962428416, 'soils': 983196900, 'metadata': 1686733598, 'seedling': 2141390901, 'sapling': 1728276363 } def biomass (h,dbh,a,b): allometry = {'WO': {'cname':'white fir', 'sname': 'Abies concolor', 'alom_src': 'globallom_id': 12323, 'meas_unit': 'cm', 'eqn': 1}} tData = ut.gData(mDataKey, sheets['treedata']) tData.to_sql('treedata', ut.eng(), if_exists = 'replace')
import util as ut import pandas as pd url = 'http://www.fs.fed.us/ne/global/pubs/books/dia_biomass/' tables = {'Table3_GTR-NE-319.xls': {'sheet' :1, 'header':2}, 'Table4_GTR-NE-319.xls': {'sheet' :0, 'header':2}, 'Table5_GTR-NE-319.xls': {'sheet' :0, 'header':2}, 'Table6_GTR-NE-319.xls': {'sheet' :0, 'header':2}, 'Table7_GTR-NE-319.xls': {'sheet' :0, 'header':2}, 'Table9_GTR-NE-319.xls': {'sheet' :0, 'header':2}} for k in tables.keys(): df = pd.read_excel(url+k, sheetname=tables[k]['sheet'], header = tables[k]['header']) df.columns = [i.replace(' ',''). replace('.','_'). replace('(','_'). replace(')','_').lower() for i in df.columns] df.to_sql(k.split('_')[0].lower(), ut.eng(), if_exists = 'replace')