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Comparing two excel files with Python based on changes

+3
−1

I have two tables:

Table1:

Name Description Amount
123 Description123 123
456 Description456 456
789 Description789 666
101 Description777 101
133 Description133 133

Table2:

Name Description Amount
456 Description456 456
789 Description789 789
101 Description101 101
123 Description123 123
102 Description102 102

I need to find the difference in Table1 compared it from Table2. The connection between these 2 excel files will be the column Name. Expected output is if something is changed in Table 2 the data must be used from Table 2 and if there is new rows from Table 2 they must be added to the final result. If nothing is also changed or Table 2 doesn't have any data for specific Name from Table 1 like 133 the rows also need to be added to the final result.

Expected output:

Name Description Amount
123 Description123 123
456 Description456 456
789 Description789 789
101 Description101 101
102 Description102 102
133 Description133 133

Thanks in advance!

Edit1: I struggle to find the solution. I understand how to compare each rows in the excel files, but they need to have exactly the same order in Name column. I don't know how to do it if there is no order like this specific case above.

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2 comment threads

Thanks for the proposal Alexei, I will try it! Also I think from here I can try: https://pandas.pyda... (1 comment)
Use some sort of maps or dictionaries (1 comment)

1 answer

+1
−0

Here's what I'd do, hope it still helps someone:

import pandas as pd

t1 = [123,456,789,101,133]
t1_descr = ['Description' + str(i) for i in t1]

table1 = pd.DataFrame({'name': t1, 'description': t1_descr, 'amount': [123,456,666,101,133]})

t2 = [456,789,101,123,102]
t2_descr = ['Description' + str(i) for i in t2]

table2 = pd.DataFrame({'name': t2, 'description': t2_descr, 'amount': t2})

df = table1.merge(table2, on=['name'], how='outer', suffixes=('_t1', '_t2'), indicator=True)

- name description_t1 amount_t1 description_t2 amount_t2 _merge
0 123 Description123 123.0 Description123 123.0 both
1 456 Description456 456.0 Description456 456.0 both
2 789 Description789 666.0 Description789 789.0 both
3 101 Description101 101.0 Description101 101.0 both
4 133 Description133 133.0 NaN NaN left_only
5 102 NaN NaN Description102 102.0
# If `name` is on both tables, use table2
df2 = df.copy()
df2.loc[df2._merge=='both', 'description'] = df2.loc[df2._merge=='both', 'description_t2']
df2.loc[df2._merge=='both', 'amount'] = df2.loc[df2._merge=='both', 'amount_t2']
# New rows on table2
df2.loc[df2._merge=='right_only', 'description'] = df2.loc[df2._merge=='right_only', 'description_t2']
df2.loc[df2._merge=='right_only', 'amount'] = df2.loc[df2._merge=='right_only', 'amount_t2']
# If `name` not in table2, use table1
df2.loc[df2._merge=='left_only', 'description'] = df2.loc[df2._merge=='left_only', 'description_t1']
df2.loc[df2._merge=='left_only', 'amount'] = df2.loc[df2._merge=='left_only', 'amount_t1']

df2.drop(columns=['description_t1', 'amount_t1', 'description_t2', 'amount_t2', '_merge'])
name description amount
0 123 Description123 123.0
1 456 Description456 456.0
2 789 Description789 789.0
3 101 Description101 101.0
4 133 Description133 133.0
5 102 Description102 102.0
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