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

## Project description

CSV Match
=========

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

Part of a set of terminal-based CSV tools, also including [CSV Pivot] (https://github.com/maxharlow/csvpivot) and [CSV Bar] (https://github.com/maxharlow/csvbar).

Tested on Python 2.7 and 3.5.

Installing
----------

pip install csvmatch

Usage
-----

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:

bash
$csvmatch data1.csv data2.csv \ --fields1 name \ --fields2 'Person Name'  You can also compare multiple columns, so if we wanted to find which name and location combinations are in both files we could: bash$ 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 strip non-alphanumeric characters (-a) 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:

bash
$csvmatch data1.csv data2.csv \ --fields1 name location \ --fields2 'Person Name' Location \ --output 1.name '2.Person Name' 2.Location \ > results.csv  There are also some special column definitions. 1* and 2* expand into all columns from that file. By default the two files are linked using an inner join -- only successful matches are returned. However using -f you can specify a left-outer join which will return everything from the first file, whether there was a match or not. You can also specify right-outer to do the same but for the second file, and full-outer to return everything from both files. We can combine some of the above options to perform operations alike Excel's VLOOKUP. So if we wanted to add a column to data2.csv giving the codename of each person that is specified in data1.csv: bash$ 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] (https://github.com/datamade/dedupe) 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.

bash
$csvmatch data1.csv data2.csv --fuzzy  The more examples you give it, the better the results will be. At minimum, you should try to provide 10 positive matches and 10 negative matches. #### Levenshtein [Damerau-Levenshtein] (https://en.wikipedia.org/wiki/Damerau–Levenshtein_distance) is a string distance metric, which counts the number of changes that would have to be made to transform one string into another. For two strings to be considered a match, we require 60% of the longer string to be the same as the shorter one. bash$ csvmatch data1.csv data2.csv --fuzzy levenshtein

name,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.

#### Metaphone

[Double Metaphone] (https://en.wikipedia.org/wiki/Metaphone#Double_Metaphone) is a phonetic matching algorithm, which compares strings based on how they are pronounced:

bash
\$ csvmatch data1.csv data2.csv --fuzzy metaphone

name,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] (https://en.wikipedia.org/wiki/List_of_most_popular_given_names) 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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