uweziegenhagen.de

import sys
import collections
import pandas as pd
from tabulate import tabulate
 
 
file1 = pd.read_csv('file1.csv', sep=';', encoding='UTF-8')
file2 = pd.read_csv('file2.csv', sep=';', encoding='UTF-8')
 
columnnames1 = list(file1)
columnnames2 = list(file2)
 
if collections.Counter(columnnames1) == collections.Counter(columnnames2):
    print ("Number of columns and Names match, Comparison possible...\n\n")
else:
    print ("Number of columns and Names are not matching!!! Please check the input!")
    sys.exit('Error!')
 
# add suffixes to distinguish between actual and expected in the merger
file1 = file1.add_suffix('_e') # expected
file2 = file2.add_suffix('_t') # t
 
 
# merge them using the given key, use outer join
comparison = pd.merge(file1,file2, how='outer',
                      left_on=['Key_e'],
                      right_on=['Key_t'])
 
# create the columnwise comparison
for col in columnnames1:
    comparison[(col + '_c')] = comparison[(col + '_t')] == comparison[(col + '_e')]
 
# reorder the columns
comparison=comparison.reindex(sorted(comparison.columns),axis=1)
 
print(tabulate(comparison, tablefmt="pipe", headers="keys"))
 
 
# save the result as Excel file
comparison.to_excel('result.xlsx')
 
# names of the comparison column
check_colnames= [s + '_c' for s in columnnames1]
 
# initialize an empty dataframe for the log
logdf=pd.DataFrame(index=[True,False])
 
for column in check_colnames:
    t=comparison[column].value_counts() # returns a series
    tt=pd.DataFrame(t) # makes a DF out of the series
    logdf = logdf.join(tt,how='outer') # join the two dfs
 
# transpose for better readability
logdf = logdf.transpose()
 
# Ensure fixed sequence of the columns
logdf=logdf.reindex(sorted(logdf.columns),axis=1)
 
# write to disk
logdf.to_excel('logfile.xlsx')
 
# for better viewing on the screen
logdf.fillna('-',inplace=True)
pd.options.display.float_format = '{:,.0f}'.format
 
print(tabulate(logdf, tablefmt="pipe", headers="keys"))