Skip to main content

Toolkit for performing fuzzy joins with Symspell framework

Project description


FuzzyPanda was created to support fuzzy join operations with Pandas DataFrames using Python Ver. 3. These fuzzy joins are a form of approximate string matching to join relational data that contain "errors" or minor modifications that preclude direct string comparison.

FuzzyPanda will match strings that

  1. Are within a user-specified edit distance (e.g. "test" == "taste" with edit distance 2)
  2. Are independent of case (e.g. "Test" == "test")
  3. Are Whitespace-delimited strings are matched regardless of token order (e.g. "dark and stormy night" == "stormy and dark night")
  4. Are independent of special symbols (e.g. "this-string" == "this string")

The criteria in steps 2-4 can be modified via modification of the fuzzypanda.preprocess.PreProcessor class.

The primary API is the fuzzypanda.matching.get_fuzzy_columns function that takes two Pandas DataFrames and a set of column names, and creates a new column in the "left" DataFrame that contains the closest entries by string edit distance to the associated values in the "right" DataFrame columns. The Pandas merge or join functions can later be used to perform full joins on the DataFrames.


FuzzyPanda can be installed using pip:

pip install fuzzypanda


This version of FuzzyPanda currently supports the fuzzypanda.matching.get_fuzzy_columns function. More functions are expected in future releases.

Create Fuzzy Matched Columns

Main fuzzy joining API for the fuzzy joining of the given left_dataframe and right_dataframe. Given a string or list of strings to the cols argument, this function will add fuzzy columns to the left_dataframe that best match the columns of the right_dataframe. This operation can then be followed up with a Pandas merge or join to perform the actual joining operation.

  • fuzzypanda.matching.get_fuzzy_columns Arguments:
    • left_dataframe (pandas.DataFrame): left Pandas dataframe to which columns will be added
    • right_dataframe (pandas.DataFrame): right Pandas dataframe from which fuzzy values in the left_dataframe will be compared and suggested
    • left_cols (List(str)): A list of strings of column names present in left_dataframe that will be compared to the corresponding columns in right_dataframe.
    • right_cols (List(str)): A list of strings of column names present in right_dataframe used for comparison to those in given in left_dataframe. If both dataframes share the column names on which fuzzy columns will be created, this parameter can be set to None and the values given in left_cols will be used as the names in both dataframes. Default is None.
    • null_return (string): The string used if a match isn't found. Can be used to set NULL values if a fuzzy match isn't found in the right_dataframe. Setting to None will return the string used to search for the fuzzy value. Default is None.
    • preprocesser: an instance of the fuzzypanda.preprocess.PreProcessor class containing the preprocess method used to pre-process the input strings. If set to None, will instantiate the default pre-processor. This option can be used to create a custom pre-processor to pass to the get_fuzzy_columns function. Default is None
    • max_edit_distance (int): The maximum edit distance that will be considered when comparing columns. The higher the number, the more "incorrect" the left_dataframe columns can be to be searched in the right_dataframe columns. Increasing this number heavily impacts runtime and should be set as low as possible. Default is 2.
  • Returns: Performs an in-place creation of fuzzy columns within left_dataframe. Each given left column in left_cols will have a 'fuzzy_' + left_col_name corresponding to the matched column.

get_fuzzy_columns Example

Suppose you wish to join the following two dataframes on columns col_1 and col_2, where the columns in left_df contain entries that are misspelled and/or jumbled tokens of those in right_df:

>        ID              col_1            col_2
> 0  123314             kitten             oboe
> 1  123213             siting          trvmpet
> 2   43543  the times of best  over te rainbow 
> 3   35435    the worst times    in Symphony C 
> 4     987       not in there     not in there

>          ID               col_1             col_2
> 0  12783314              kitten              oboe
> 1  12352213             sitting           trumpet
> 2  43233543   the best of times  over the rainbow
> 3  23432420  the worst of times    Symphony in C#

We can now call fuzzypanda.matching.get_fuzzy_columns. Notice that the results are columns added to left_df in-place, rather than returning a new DataFrame.

                      		left_cols=['col_1', 'col_2'])

>        ID              col_1            col_2         fuzzy_col_1 \
> 0  123314             kitten             oboe              kitten   
> 1  123213             siting          trvmpet             sitting   
> 2   43543  the times of best  over te rainbow   the best of times   
> 3   35435    the worst times    in Symphony C  the worst of times   
> 4     987       not in there     not in there        not in there
>         fuzzy_col_2  
> 0              oboe  
> 1           trumpet  
> 2  over the rainbow  
> 3    Symphony in C#  
> 4      not in there 


This package uses the Symspell Python port symspellpy by mammothb of the original C# implementation of Symspell by Wolf Garbe. This fuzzy column creation approach applies a Pandas-friendly wrapper around the Symspell Symmetric Delete spelling correction algorithm to allow substantially faster fuzzy joining. Tools such as fuzzywuzzy will run in Omega(mn) to find the best-matching strings in a column of n values compared to the m values of another column, whereas this model is expected to have a runtime of Omega(m + n) due to the pre-processing of the right DataFrame columns as a spellchecker corpus that searched using the Symmetric Delete spelling correction algorithm.

This method is best suited for fuzzy searches of large DataFrames due to the comparatively large amount of pre-processing but faster search performance.

The algorithm operates as follows:

  1. A "left" Pandas DataFrame and a "right" Pandas DataFrame are input to get_fuzzy_columns with the column names used for comparison.
  2. Each right DataFrame is copied into a temporary corpus text file.
  3. Each entry in the corpus text file is preprocessed using either the default fuzzypanda.preprocess.PreProcessor or a user-supplied object containing a preprocess method and copied to another preprocessed text file. An in-memory index is created to translate processed strings to preprocessed strings.
  4. A symspellpy object is instantiated and the corpus file is used to create a lookup dictionary.
  5. Each record from the left DataFrame is preprocessed and queried from the dictionary using the symspellpy.lookup function to find the closest string in terms of edit distance, and the suggested string (or a substitute string if one isn't found) is placed in an intemediate list.
  6. When all records of the left DataFrame have been processed, a new column containing the results of the fuzzy lookup is added to the left DataFrame in a column labeled 'fuzzy_' + queried column name.

Future Work

  • Directly implement pandas merge and join
  • Replace symspellpy with a C++ implementation of Symspell to speed lookup calculations
  • Create option for multiprocessing and multithreading column record queries.
  • Add API to directly process CSV files
  • Add API to use Pandas DataFrame chunks
  • Expand functionality to use SparkSQL DataFrames

Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for fuzzypanda, version 0.1.1
Filename, size File type Python version Upload date Hashes
Filename, size fuzzypanda-0.1.1-py3-none-any.whl (12.3 kB) File type Wheel Python version py3 Upload date Hashes View
Filename, size fuzzypanda-0.1.1.tar.gz (13.1 kB) File type Source Python version None Upload date Hashes View

Supported by

Pingdom Pingdom Monitoring Google Google Object Storage and Download Analytics Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN DigiCert DigiCert EV certificate StatusPage StatusPage Status page