If two tables have the same column, then we can merge them into a single table by matching rows.
Thanks to the National Centers for Environmental Information for collecting temperature and precipitation data by state.
{ a_table : Table 1 ? Table to Include in Merge }
{ b_table : Table 2 ? Table to Include in Merge }
{ key_column_name : Key Column ? Column to Use for Merge }
# CrossCompute
a_table_path = 'usa-temperature-by-state.csv'
b_table_path = 'usa-precipitation-by-state.csv'
key_column_name = 'State'
target_folder = '/tmp'
import pandas as pd
a_table = pd.read_csv(a_table_path)
b_table = pd.read_csv(b_table_path)
c_table = pd.merge(a_table, b_table, on=key_column_name)
from os.path import join
target_path = join(target_folder, 'table.csv')
c_table.to_csv(target_path, index=False)
print('c_table_path = ' + target_path)
{ c_table : Merged Table }