import pandas as pd
a = pd.DataFrame({'x': [1,2,3], 'y' : ['one', 'two', 'three']})
b = pd.DataFrame({'x': [1, 3], 'f' : ['so', 'to']}) 
pd.merge(a,b, on='x', how='outer')  # merge by x-column, u can use date column instead. 
# set of data frames (tables)
from functools import reduce # isn't  needed in Python 2.x
# merge = lambda x, y: pd.merge(x, y, on='common_column_name', how='outer')
#example
c = pd.DataFrame({'x': [2, 3],  'c': [8, 9]})
merge = lambda x, y: pd.merge(x, y, on='x', how='outer')
merged_df = reduce(merge, [a ,b, c]) # merge all dfs at once

  
#!/usr/bin/env python3
 
import pandas as pd
 
a = pd.read_csv('file1.csv')
b = pd.read_csv('file2.csv')
c = pd.merge(a, b, on='Date', how='outer')
c.to_csv('file3.csv', index=False, line_terminator='\r\n')