Algorithm used in Excel Fuzzy Lookup
The following is an excerpt from Microsoft Fuzzy Lookup Add-In for Excel, Readme.docx. I hope that helps.

Advanced Concepts
Fuzzy Lookup technology is based upon a very simple, yet flexible
  measure of similarity between two records. Jaccard similarity  Fuzzy
  Lookup uses Jaccard similarity, which is defined as the size of the
  set intersection divided by the size of the set union for two sets of
  objects. For example, the sets {a, b, c} and {a, c, d} have a Jaccard
  similarity of 2/4 = 0.5 because the intersection is {a, c} and the
  union is {a, b, c, d}. The more that the two sets have in common, the
  closer the Jaccard similarity will be to 1.0. 
Weighted Jaccard similarity and tokenization of records  With Fuzzy
  Lookup, you can assign weights to each item in a set and define the
  weighted Jaccard similarity as the total weight of the intersection
  divided by the total weight of the union. For the weighted sets {(a,
  2), (b, 5), (c, 3)}, {(a, 2), (c, 3), (d, 7)}, the weighted Jaccard
  similariyt is (2 + 3)/(2 + 3 + 5 +7) = 5/17 = .294. 
Because Jaccard similarity is defined over sets, Fuzzy Lookup must
  first convert data records to sets before it calculates the Jaccard
  similarity. Fuzzy Lookup converts the data to sets using a Tokenizer.
  For example, the record {“Jesper Aaberg”, “4567 Main Street”} might be
  tokenized into the set, {“ Jesper”, “Aaberg”, “4567”, “Main”,
  “Street”}. The default tokenizer is for English text, but one may
  change the LocaleId property in Configure=>Global Settings to specify
  tokenizers for other languages.
Token weighting  Because not all tokens are of equal importance, Fuzzy
  Lookup assigns weights to tokens. Tokens are assigned high weights if
  they occur infrequently in a sample of records and low weights if they
  occur frequently. For example, frequent words such as “Corporation”
  might be given lower weight, while less frequent words such as
  “Abracadabra” might be given a higher weight. One may override the
  default token weights by supplying their own table of token weights.
Transformations Transformations greatly increase the power of Jaccard
  similarity by allowing tokens to be converted from one string to
  another. For instance, one might know that the name “Bob” can be
  converted to “Robert”; that “USA” is the same as “United States”; or
  that “Missispi” is a misspelling of “Mississippi”. There are many
  classes of such transformations that Fuzzy Lookup handles
  automatically such as spelling mistakes (using Edit Transformations
  described below), string prefixes, and string merge/split operations.
  You can also specify a table containing your own custom
  transformations.
Jaccard similarity under transformations  The Jaccard similarity under
  transformations is the maximum Jaccard similarity between any two
  transformations of each set. Given a set of transformation rules, all
  possible transformations of the set are considered. For example, for
  the sets {a, b, c} and {a, c, d} and the transformation rules {b=>d,
  d=>e}, the Jaccard similarity is computed as follows: Variations of
  {a, b, c}: {a, b, c}, {a, d, c} Variations of {a, c, d}: {a, c, d},
  {a, c, e} Maximum Jaccard similarity between all pairs: J({a, b, c},
  {a, c, d}) = 2/4 = 0.5 J({a, b, c}, {a, c, e}) = 2/4 = 0.5 J({a, d,
  c}, {a, c, d}) = 3/3 = 1.0 J({a, d, c}, {a, c, e}) = 2/4 = 0.5 The
  maximum is 1.0. Note: Weghted Jaccard similiary under transformations
  is simply the maximum weighted Jaccard similarity across all pairs of
  transformed sets. 
Edit distance  Edit distance is the total number of character
  insertions, deletions, or substitutions that it takes to convert one
  string to another. For example, the edit distance between “misissipi”
  and “mississippi” is 2 because two character insertions are required.
  One of the transformation providers that’s included with Fuzzy Lookup
  is the EditTransformationProvider, which generates specific
  transformations for each input record and creates a transformation
  from the token to all words in its dictionary that are within a given
  edit distance. The normalized edit distance is the edit distance
  divided by the length of the input string. In the previous example,
  the normalized edit distance is 2/9 = .222.
I was working on matching company names of two sets. I was trying to code it in Python with Levenstien's distance. I was having issues with short names of companies, and their trailing part like Pvt,Ltd. I have ran the same set with Excel Fuzzy lookup and was getting good results. I there a way that i can see how excel fuzzy lookup is coded and use the same implementation in python.