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Find (fuzzy) matches between two CSV files in the terminal.

Project description

Find (fuzzy) matches between two CSV files in the terminal.

Tested on Python 3.6 and 2.7.

[There’s a tutorial on how CSV Match can be used for investigative journalism here.](


pip install csvmatch


Say you have one CSV file such as:

name,location,codename George Smiley,London,Beggerman Percy Alleline,London,Tinker Roy Bland,London,Soldier Toby Esterhase,Vienna,Poorman Peter Guillam,Brixton,none Bill Haydon,London,Tailor Oliver Lacon,London,none Jim Prideaux,Slovakia,none Connie Sachs,Oxford,none

And another such as:

Person Name,Location Maria Andreyevna Ostrakova,Russia Otto Leipzig,Estonia George SMILEY,London Peter Guillam,Brixton Konny Saks,Oxford Saul Enderby,London Sam Collins,Vietnam Tony Esterhase,Vienna Claus Kretzschmar,Hamburg

You can then find which names are in both files:

$ csvmatch data1.csv data2.csv
–fields1 name –fields2 ‘Person Name’

You can also compare multiple columns, so if we wanted to find which name and location combinations are in both files we could:

$ csvmatch data1.csv data2.csv
–fields1 name location –fields2 ‘Person Name’ Location

By default, all columns are used to compare rows. Specific columns can be also be given to be compared – these should be in the same order for both files. Column headers with a space should be enclosed in quotes. Matches are case-sensitive by default, but can be made case-insensitive with -i.

There are also options to ignore non-alphanumeric characters (-a), to convert to the latin alphabet (-n), and to sort words (-s) before comparisons. Specific terms can also be filtered out before comparisons by passing a text file and the -l argument. A predefined list of common English name prefixes (Mr, Ms, etc) can be used with -t.

By default the columns used in the output are the same ones used for matching. Other sets of columns can be specified using the –output parameter. This takes a space-separated list of column names, each prefixed with a number and a dot indicating which file that field is from:

$ csvmatch data1.csv data2.csv
–fields1 name location –fields2 ‘Person Name’ Location –output ‘2.Person Name’ 2.Location > results.csv

There are also some special column definitions. 1* and 2* expand into all columns from that file. Where a fuzzy matching algorithm has been used degree will add a column with a number between 0 - 1 indicating the strength of each match.

By default the two files are linked using an inner join – only successful matches are returned. However using -f you can specify a left-outer join which will return everything from the first file, whether there was a match or not. You can also specify right-outer to do the same but for the second file, and full-outer to return everything from both files.

We can combine some of the above options to perform operations alike Excel’s VLOOKUP. So if we wanted to add a column to data2.csv giving the codename of each person that is specified in data1.csv:

$ csvmatch data1.csv data2.csv
–fields1 name –fields2 ‘Person Name’ –join right-outer –output 2* 1.codename > results.csv

### Fuzzy matching

CSV Match also supports fuzzy matching. This can be combined with any of the above options.

#### Bilenko

The default fuzzy mode makes use of the [Dedupe library]( built by Forest Gregg and Derek Eder based on the work of Mikhail Bilenko. This algorithm asks you to give a number of examples of records from each dataset that are the same – this information is extrapolated to link the rest of the dataset.

$ csvmatch data1.csv data2.csv –fuzzy

The more examples you give it, the better the results will be. At minimum, you should try to provide 10 positive matches and 10 negative matches.

#### Levenshtein

[Damerau-Levenshtein](–Levenshtein_distance) is a string distance metric which counts the number of changes that would have to be made to transform one string into another.

For two strings to be considered a match, we require 60% of the longer string to be the same as the shorter one. This threshold can be modified by passing a number between 0.0 and 1.0 with -t.

$ csvmatch data1.csv data2.csv –fuzzy levenshtein

name,Person Name George Smiley,George SMILEY Toby Esterhase,Tony Esterhase Peter Guillam,Peter Guillam

Here this matches Toby Esterhase and Tony Esterhase – Levenshtein is good at picking up typos and other small differences in spelling.

### Jaro

[Jaro-Winkler](–Winkler_distance) is a string distance metric which counts the number of transpositions that would be required to transform one string into another. It tends to work better than Levenshtein for shorter strings of text.

$ csvmatch data1.csv data2.csv –fuzzy jaro

name,Person Name George Smiley,George SMILEY Percy Alléline,Peter Guillam Percy Alléline,Sam Collins Toby Esterhase,Tony Esterhase Peter Guíllam,Peter Guillam Connie Sachs,Konny Saks

Here we can see a couple of incorrect matches for Percy Alléline, but Connie Sachs has matched.

#### Metaphone

[Double Metaphone]( is a phonetic matching algorithm, which compares strings based on how they are pronounced:

$ csvmatch data1.csv data2.csv –fuzzy metaphone

name,Person Name George Smiley,George SMILEY Peter Guillam,Peter Guillam Connie Sachs,Konny Saks

This shows a match for Connie Sachs and Konny Saks, despite their very different spellings.

A note on uniqueness

Both with exact matches and fuzzy matching a name being the same is [no guarantee]( it refers to the same person. But the inverse is also true – even with CSV Match, a combination of first inital and last name is likely to be sufficiently different from forename, middle names, and surname together that a match is unlikely. Moreso if one name includes a typo, either accidential or deliberate.

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