Package: textmatch
TextmatchFind fuzzy matches between datasets.Fuzzy matching is the art and science of connecting up bits of information that are written differently but represent the samething– such as a person or company.Installing$ pip install textmatchGetting startedThe best way to approach fuzzy matching with Textmatch is to start with an exact match. From there, you can incrementally improve the results by telling Textmatch 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 datasets can be dataframes fromPyArrow,Pandas, orPolars. The output results will be in PyArrow format – which can then be converted to Pandas withmatches.to_pandas(), or to Polars withpolars.from_arrow(matches).Exampleimporttextmatchdata1:nameplacecodenameSam CollinsVietnamnoneRoy BlandLondonSoldierGeorge SmileyLondonBeggermanBill HaydonLondonTailorPerçy AllélíneLondonTinkerKretzschmarHamburgnoneOliver LaconLondonnoneJim PrideauxSlovakianonePeter Guillam EsqBrixtonnoneToby EsterhaseViennaPoormanConnie SachsOxfordnonedata2:Person NameLocationMaria Andreyevna OstrakovaRussiaKonny SaksOxfordTony EsterhaseViennaPeter GuillamBrixtonMr Jim PrideauxCzech RepublicLacon OliverCambridgeClaus KretzschmarHamburgRichard BlandLondonRoy RodgersRomaniaPercy AllelineLondonBill-HaydonLondonGeorge SMILEYLondonRoy BlandUKSam CollinsVietnamTo run an exact match on thenamecolumn from the first dataset againstPerson Namefrom the second:textmatch.run(data1,data2,fields1=[['name']],fields2=[['Person Name']])The resulting matches include the two names which are written exactly the same:nameplacecodenamePerson NameLocationRoy BlandLondonSoldierRoy BlandUKSam CollinsVietnamnoneSam CollinsVietnamMatches aremany-to-many, ie. it is possible for one row in the first dataset 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.UsageTextmatch has one function,run, which accepts the first dataset followed by the second. All other arguments are optional, described below:FieldsThefields1andfields2arguments accept two-dimensional lists of column names that should be used for the match. These should be in the same order for both datasets – the first column specified for the first dataset will be compared against the first column specified for the second dataset, and so on. The columns can only contain strings. Defaults to comparing all columns. The outer list is used forblocking.ExampleTo match on thenameandplacecolumns from the first dataset againstPerson NameandLocationfrom the second:textmatch.run(data1,data2,fields1=[['name','place']],fields2=[['Person Name','Location']])The resulting matches include the single name-place pair which is the same in both datasets:nameplacecodenamePerson NameLocationSam CollinsVietnamnoneSam CollinsVietnamIgnoranceTheignoresargument accepts a two-dimensional list of characteristics which should be disregarded for two records to be considered a match. The outer list is used forblocking.Combining different forms of ignorance can be quite powerful. The order in which you specify them is not significant.caseignores how text is capitalised.Exampletextmatch.run(data1,data2,fields1=[['name']],fields2=[['Person Name']],ignores=[['case']])The resulting matches include George Smiley, whose surname is in all-capitals in the second dataset:nameplacecodenamePerson NameLocationGeorge SmileyLondonBeggermanGeorge SMILEYLondonRoy BlandLondonSoldierRoy BlandUKSam CollinsVietnamnoneSam CollinsVietnamnonalphaignores anything that isn't a number or a letter.Exampletextmatch.run(data1,data2,fields1=[['name']],fields2=[['Person Name']],ignores=[['nonalpha']])The resulting matches include Bill Haydon, whose name is written with a hypen in the second dataset:nameplacecodenamePerson NameLocationBill HaydonLondonTailorBill-HaydonLondonRoy BlandLondonSoldierRoy BlandUKSam CollinsVietnamnoneSam CollinsVietnamnonlatinignores nonlatin characters – so a character such as anéwill be seen as if it were ane.Exampletextmatch.run(data1,data2,fields1=[['name']],fields2=[['Person Name']],ignores=[['nonlatin']])The resulting matches include Percy Alleline, whose name is written with several diacritics in the first dataset:nameplacecodenamePerson NameLocationPerçy AllélíneLondonTinkerPercy AllelineLondonRoy BlandLondonSoldierRoy BlandUKSam CollinsVietnamnoneSam CollinsVietnamwords-leadingignores all words except the last. This is useful for matching on surnames only.Exampletextmatch.run(data1,data2,fields1=[['name']],fields2=[['Person Name']],ignores=[['words-leading']])The resulting matches include Toby and Tony Esterhase, Jim Prideaux and Mr Jim Prideaux, Kretzschmar and Claus Kretzschmar, as well as Roy and Richard Bland:nameplacecodenamePerson NameLocationToby EsterhaseViennaPoormanTony EsterhaseViennaJim PrideauxSlovakianoneMr Jim PrideauxCzech RepublicKretzschmarHamburgnoneClaus KretzschmarHamburgRoy BlandLondonSoldierRichard BlandLondonRoy BlandLondonSoldierRoy BlandUKSam CollinsVietnamnoneSam CollinsVietnamwords-tailingignore all words except the first.Exampletextmatch.run(data1,data2,fields1=[['name']],fields2=[['Person Name']],ignores=[['words-tailing']])The resulting matches include Peter Guillam Esq and Peter Guillam, Roy Bland and Roy Rodgers, as well as the two capitalisations of George Smiley:nameplacecodenamePerson NameLocationPeter Guillam EsqBrixtonnonePeter GuillamBrixtonRoy BlandLondonSoldierRoy RodgersRomaniaGeorge SmileyLondonBeggermanGeorge SMILEYLondonRoy BlandLondonSoldierRoy BlandUKSam CollinsVietnamnoneSam CollinsVietnamwords-orderignores the order in which the words are given. This is useful for matching names given surname-first with those given forename-first.Exampletextmatch.run(data1,data2,fields1=[['name']],fields2=[['Person Name']],ignores=[['words-order']])The resulting matches include Oliver Lacon, whose name is written surname-first in the second dataset:nameplacecodenamePerson NameLocationOliver LaconLondonnoneLacon OliverCambridgeRoy BlandLondonSoldierRoy BlandUKSam CollinsVietnamnoneSam CollinsVietnamtitlesignores common English name prefixes such as Mr and Ms. There is a full list of these titleshere.Exampletextmatch.run(data1,data2,fields1=[['name']],fields2=[['Person Name']],ignores=[['titles']])The resulting matches include Jim Prideaux, who has the title 'Mr' in the second dataset:nameplacecodenamePerson NameLocationJim PrideauxSlovakianoneMr Jim PrideauxCzech RepublicRoy BlandLondonSoldierRoy BlandUKSam CollinsVietnamnoneSam CollinsVietnamregexignores terms specific to your data using a given regular expression. This is specified inline:regex=EXPRESSION.ExampleTo use the regular expressionEsq$to ignore the word 'Esq' where it appear at the end of a value:textmatch.run(data1,data2,fields1=[['name']],fields2=[['Person Name']],ignores=[['regex= Esq$']])The resulting matches include Peter Guillam, who has the name suffix 'Esq' in the first dataset:nameplacecodenamePerson NameLocationPeter Guillam EsqBrixtonnonePeter GuillamBrixtonRoy BlandLondonSoldierRoy BlandUKSam CollinsVietnamnoneSam CollinsVietnamMethods & thresholdsThemethodsargument accepts a list of methods. This lets you specify the algorithm which is used to do the matching. Subsequent items in the list are used forblocking.There are three different categories of method:Comparedmethods work by comparing every row from the first dataset 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 datasets. However, they are still useful for larger matches when usingblocking.Appliedmethods 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.Custommethods have their own individual approach. Textmatch 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 thethresholdargument, which accepts a list of numbers between 0.0 and 1.0, defaulting to 0.6. Subsequent items in the list are also used forblocking.You can also include the matching degree number as a column by specifying it in theoutputs.[!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 bythe birthday paradox.literalis the default – it matches strings exactly, after ignored characteristics have been taken into account.levenshteinUses theDamerau-Levenshteinstring 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.Exampletextmatch.run(data1,data2,fields1=[['name']],fields2=[['Person Name']],methods=['levenshtein'])The resulting matches include various names with small typographical differences, though the most emblematic of this matching method would be Toby and Tony Esterhase:nameplacecodenamePerson NameLocationSam CollinsVietnamnoneSam CollinsVietnamRoy BlandLondonSoldierRoy BlandUKGeorge SmileyLondonBeggermanGeorge SMILEYLondonBill HaydonLondonTailorBill-HaydonLondonPerçy AllélíneLondonTinkerPercy AllelineLondonKretzschmarHamburgnoneClaus KretzschmarHamburgJim PrideauxSlovakianoneMr Jim PrideauxCzech RepublicPeter Guillam EsqBrixtonnonePeter GuillamBrixtonToby EsterhaseViennaPoormanTony EsterhaseViennajarouses theJaro-Winklerstring 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.Exampletextmatch.run(data1,data2,fields1=[['name']],fields2=[['Person Name']],methods=['jaro'])The resulting matches includes many more matches thanlevenshtein, though also many more false positives:nameplacecodenamePerson NameLocationSam CollinsVietnamnonePercy AllelineLondonSam CollinsVietnamnoneSam CollinsVietnamRoy BlandLondonSoldierRichard BlandLondonRoy BlandLondonSoldierRoy RodgersRomaniaRoy BlandLondonSoldierBill-HaydonLondonRoy BlandLondonSoldierRoy BlandUKGeorge SmileyLondonBeggermanGeorge SMILEYLondonBill HaydonLondonTailorBill-HaydonLondonBill HaydonLondonTailorRoy BlandUKPerçy AllélíneLondonTinkerPeter GuillamBrixtonPerçy AllélíneLondonTinkerPercy AllelineLondonKretzschmarHamburgnoneClaus KretzschmarHamburgJim PrideauxSlovakianoneMr Jim PrideauxCzech RepublicPeter Guillam EsqBrixtonnonePeter GuillamBrixtonToby EsterhaseViennaPoormanTony EsterhaseViennaToby EsterhaseViennaPoormanRoy RodgersRomaniaConnie SachsOxfordnoneKonny SaksOxfordmetaphoneuses theDouble Metaphonephonetic 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.Exampletextmatch.run(data1,data2,fields1=[['name']],fields2=[['Person Name']],methods=['metaphone'])The resulting matches includes those with nonalphanumeric differences, as well as Connie Sachs and Konny Saks, names written quite differently that would be pronounced the same:nameplacecodenamePerson NameLocationConnie SachsOxfordnoneKonny SaksOxfordRoy BlandLondonSoldierRoy BlandUKGeorge SmileyLondonBeggermanGeorge SMILEYLondonSam CollinsVietnamnoneSam CollinsVietnamBill HaydonLondonTailorBill-HaydonLondonbilenkousesDedupe, 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 dataset. 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.BlockingBlocking 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 datasets.In a 'regular' match, you are really just matching using a single block. Each block is defined by: a list of fields for each dataset, a list of ignores, a method, and a threshold. To perform a blocked match Textmatche 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.ExampleTo specify a first block that does a case-insensitive literal match on surnames, then a second block performing a Levenshtein match on forenames:textmatch.run(data1,data2,fields1=[['name']],fields2=[['Person Name']],ignores=[['case','words-leading'],['words-tailing']],methods=['literal','levenshtein'])nameplacecodenamePerson NameLocationKretzschmarHamburgnoneClaus KretzschmarHamburgGeorge SmileyLondonBeggermanGeorge SMILEYLondonRoy BlandLondonSoldierRoy BlandUKSam CollinsVietnamnoneSam CollinsVietnamOutputsTheoutputargument accepts a list of column names which should appear in the output, each prefixed with a number and a dot indicating which dataset that field is from. They are case-sensitive, and can be in any order you desire. It defaults to all columns in the first dataset, followed by all columns in the second.There are some special column definitions:1*and2*expand into all columns from the first and second datasets respectively, anddegreewill add a column with the matching degree number.ExampleTo include every column from the second dataset, followed by thecodenamecolumn from the first, followed by the matching degree:textmatch.run(data1,data2,fields1=[['name']],fields2=[['Person Name']],methods=['levenshtein'],output=['2*','1.codename','degree'])Person NameLocationcodenamedegreeSam CollinsVietnamnone1.0Roy BlandUKSoldier1.0George SMILEYLondonBeggerman0.61538464Bill-HaydonLondonTailor0.90909094Percy AllelineLondonTinker0.78571427Claus KretzschmarHamburgnone0.64705884Mr Jim PrideauxCzech Republicnone0.8Peter GuillamBrixtonnone0.7647059Tony EsterhaseViennaPoorman0.9285714Join typesThejoinargument takes a string that indicates what other nonmatching records should be included in the output. Aleft-outerjoin will return everything from the first dataset, whether there was a match or not, aright-outerto do the same but for the second dataset, and afull-outerto return everything from both datasets. Where two rows didn't match the values will be blank. Defaults to aninnerjoin, where only successful matches are returned.ExampleTo include all rows from the first dataset, but only those that match from the second:textmatch.run(data1,data2,fields1=[['name']],fields2=[['Person Name']],join='left-outer')nameplacecodenamePerson NameLocationRoy BlandLondonSoldierRoy BlandUKSam CollinsVietnamnoneSam CollinsVietnamGeorge SmileyLondonBeggermanBill HaydonLondonTailorPerçy AllélíneLondonTinkerKretzschmarHamburgnoneOliver LaconLondonnoneJim PrideauxSlovakianonePeter Guillam EsqBrixtonnoneToby EsterhaseViennaPoormanConnie SachsOxfordnoneProgress bars & alertsBy default Textmatch does not print out any details of its operations, however it is possible to to display progress bars and logging alerts by definingprogressandalertfunctions that handle these events. This is especially useful in an interactive Jupyter environment.For example, usingtqdmandipywidgets:importtqdm.notebookdefprogress(operation,total):bar=tqdm.notebook.tqdm(desc=operation,total=total,bar_format='{desc}{bar}{percentage:3.0f}%{remaining}left',dynamic_ncols=True)returnbar.updatedefalert(message,importance):print(f'[{importance.upper()}]{message}')These functions are then passed as arguments when you run Textmatch:textmatch.run(data1,data2,fields1=[['name']],fields2=[['Person Name']],progress=progress,alert=alert).to_pandas()Other librariesPythonRecordLinkageSplinkDeezyMatchDedupeDeepMatcherHlinkSkrubRLTKString GrouperPyEntityMatchingTextPackString2StringRapidFuzzTheFuzzRZoomerjoinReclin2FedmatchFastLinkFuzzyjoin