`pandas-linker` runs comparison windows over pandas DataFrame and
links the rows via assigned UUIDs. This library does not actually do any duplicate
detection but provides a harness to run your own comparison functions on your data.
Let's say you have a DataFrame like this:
[ix] | name | country
0 | Pete | Spain
1 | Mary | USA
2 | Bart | US
3 | Mary | US
and you want to detect similar rows and mark them as such. Here's how to that:
from pandas_linker import get_linker
def compare_rows(a, b):
''' Example function that decides if two rows represent same entity.'''
return a['name'] in b['name'] or b['name'] in a['name']
# df is a pandas.DataFrame with a unique index
with get_linker(df, field='uid') as linker:
print('Comparing in 10 row window sorted by name')
linker(sort_cols=['name'], window_size=10, cmp=compare_rows)
print('Comparing in 15 row window sorted by country')
linker(sort_cols=['country'], window_size=15, cmp=compare_rows)
After running the linker the process is complete
[ix] | name | country | uid
0 | Pete | Spain | 7509781940fc471cad5dc32944652d70
1 | Mary | USA | 8f8dccd91568472daf740e9160349d6c
2 | Bart | US | 12b55fbe80f64d378193acd727b0e051
3 | Mary | US | 8f8dccd91568472daf740e9160349d6c
Note that both "Mary" rows in the DataFrame have been identified as representing
the same entity and were assigned the same UUID.
TODO: Brief introduction on what you do with files - including link to relevant help section.