CSV Match

Find fuzzy matches between CSV files. Based on Textmatch.

Fuzzy matching is the art and science of connecting up bits of information that are written differently but represent the same thing – such as a person or company.

Installing

The best way to install is with pipx, which can be installed with Homebrew: brew install pipx. Then:

pipx install csvmatch

Getting started

The best way to approach fuzzy matching with CSV Match is to start with an exact match. From there, you can incrementally improve the results by telling CSV Match about relevant information that should be taken into account and irrelevant information that should be disregarded. Experiment with different approaches. It is helpful to know what the data looks like, and how it has been collected.

The input files can be in CSV or Parquet format. The output results will be a CSV file.

Matches are many-to-many, ie. it is possible for one row in the first file to match several rows in the second, and vice-versa.

[!TIP] There is a tradeoff between false negatives and false positives – it is often better to have some incorrect matches in your results that can be manually checked afterwards than to have correct matches missing.

Usage

CSV Match only requires two arguments, the first file followed by the second. All others are optional, described below:

Fields

The fields1 and fields2 arguments accept one or more column names that should be used for the match. If the column name has a space it should be wrapped in quotes. These should be in the same order for both files – the first column specified for the first file will be compared against the first column specified for the second file, and so on. Defaults to comparing all columns. This flag can be specified multiple times for blocking.

Ignorance

The ignores argument accepts one or more characteristics which should be disregarded for two records to be considered a match. This flag can be specified multiple times for blocking.

Combining different forms of ignorance can be quite powerful. The order in which you specify them is not significant.

case ignores how text is capitalised.

nonalpha ignores anything that isn't a number or a letter.

nonlatin ignores non-Latin characters – so Jérôme will match Jerome, Weiß will match Weiss, and Пушкин will match Pushkin. The further the script is from the Latin alphabet, the less accurate this transliteration will be.

words-leading ignores all words except the last. This is useful for matching on surnames only.

words-tailing ignore all words except the first.

words-order ignores the order in which the words are given. This is useful for matching names given surname-first with those given forename-first.

titles ignores common English name prefixes such as Mr and Ms. There is a full list of these titles here.

regex ignores terms specific to your data using a given regular expression. This is specified inline: regex=EXPRESSION.

Methods & thresholds

The methods argument accepts one or more methods. This lets you specify the algorithm which is used to do the matching. Multiple methods are used for blocking.

There are three different categories of method:

• Compared methods work by comparing every row from the first file with every row from the second, producing a number that represents the degree of the match. This means the amount of time required to run a match grows exponentially with the size of the input files. However, they are still useful for larger matches when using blocking.
• Applied methods transform text into a different representation before they are matched up. These methods are quicker than compared ones, though no meaningful matching degree number is generated – either they match or they don't.
• Custom methods have their own individual approach. CSV Match only has one, Bilenko. It generates a matching degree number.

For those matching methods that generate a matching degree number there is then a threshold filter for any two records to be considered to be a match – you can adjust this with the --threshold argument, which accepts one or more numbers between 0.0 and 1.0, defaulting to 0.6. Multiple thresholds are also used for blocking.

You can also include the matching degree number as a column by specifying it in the outputs.

[!WARNING] When working with names of people, exact matches, even when other pieces of information such as birthdays are included, are not a guarantee that the two names actually refer to the same human. Furthermore, the chance of a mismatch is unintuitively high – as illustrated by the birthday paradox.

literal is the default – it matches strings exactly, after ignored characteristics have been taken into account.

levenshtein Uses the Damerau-Levenshtein string distance metric that counts the number of changes that would have to be made to transform one string into another. Performs compared matching. Where two strings are of different lengths the longer string is used as the denominator for the threshold filter. Good at picking up typos and other small differences in spelling.

jaro uses the Jaro-Winkler string distance metric that counts characters in common, though it considers differences near the start of the string to be more significant than differences near the end. Performs compared matching. It tends to work better than Levenshtein for shorter strings of text.

metaphone uses the Double Metaphone phonetic encoding algorithm to convert words into a representation of how they are pronounced. Performs applied matching. Tends to work better for data which has been transcribed or transliterated.

bilenko uses Dedupe, a library built by Forest Gregg and Derek Eder based on the work of Mikhail Bilenko that will ask you to train it by asking whether different pairs of records should match. The information you give it is then extrapolated to match up the rest of the file. The more examples you give it, the better the results will be. At minimum, try to provide 10 positive matches and 10 negative matches. Performs custom matching.

Blocking

Blocking is the approach of performing multiple matches, with subsequent matches only applying to the subset of matches resulting from the previous match. This can make matches both quicker and more precise. This is an advanced topic, and can be ignored if you are happy with the quality of matches and are dealing with smaller files.

In a 'regular' match, you are really just matching using a single block. Each block is defined by: a list of fields for each file, a list of ignores, a method, and a threshold. To perform a blocked match CSV Match needs to know each of these things for each block. You specify these through list arguments, or through outer lists for those arguments where the block requires a list itself. If you specify one of these things less than the total number of blocks – such as if you had two blocks, but specified the threshold once – that value will then be used for all subsequent blocks.

Outputs

The output argument accepts a list of column names which should appear in the output, each prefixed with a number and a dot indicating which file that field is from. They are case-sensitive, and can be in any order you desire. It defaults to all columns in the first file, followed by all columns in the second.

There are some special column definitions: 1* and 2* expand into all columns from the first and second files respectively, and degree will add a column with the matching degree number.

Join types

The join argument takes a string that indicates what other nonmatching records should be included in the output. A left-outer join will return everything from the first file, whether there was a match or not, a right-outer to do the same but for the second file, and a full-outer to return everything from both files. Where two rows didn't match the values will be blank. Defaults to an inner join, where only successful matches are returned.