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import pandas as pd
import numpy as np

df = pd.read_table('species_counts.tsv', header=0, index_col=False, dtype={'country_s':str, '#_Records':np.float64, '#_Species':np.float64, 'geonames_id':np.int32})
geo = pd.read_table('geography_data.txt', header=0, index_col=False, dtype={'country_g':str, 'geonames_id':np.int32, 'area_km2':np.float64, 'percent_water':np.float64, 'population_density':np.float64, 'gdp_nominal':np.float64, 'gdp_ppp':np.float64, 'gini':np.float64, 'hdi':np.float64})
eco = pd.read_table('ecoregions.tsv', header=0, index_col=False, dtype={'ecozone':str, 'biome':str, 'ecoregion':str, 'country_e':str, 'geonames_id':np.int32})
print(list(df))
df.set_index(['geonames_id'])
geo.set_index('geonames_id')
eco.set_index('geonames_id')

all_data = pd.concat([df, geo, eco], axis=1, join_axes=[df.index])
print('complete')

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