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Fuzzy matching in pandas using csvmatch

Project description

fuzzy_pandas

A razor-thin layer over csvmatch that allows you to do fuzzy mathing with pandas dataframes.

Installation

pip install fuzzy_pandas

Usage

To borrow 100% from the original repo, 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:

import pandas as pd
import fuzzy_pandas as fpd

df1 = pd.read_csv("data1.csv")
df2 = pd.read_csv("data2.csv")

matches = fpd.fuzzy_merge(df1, df2,
                          left_on=['name'],
                          right_on=['Person Name'],
                          ignore_case=True,
                          keep='match')

print(matches)
. name Person Name
0 George Smiley George SMILEY
1 Peter Guillam Peter Guillam

Options

Dumping this out of the code itself, apologies for lack of pretty formatting.

  • left : DataFrame
  • right : DataFrame
    • Object to merge left with
  • on : str or list
    • Column names to compare. These must be found in both DataFrames.
  • left_on : str or list
    • Column names to compare in the left DataFrame.
  • right_on : str or list
    • Column names to compare in the right DataFrame.
  • left_cols : list, default None
    • List of columns to preserve from the left DataFrame.
    • Defaults to left_on.
  • right_cols : list, default None
    • List of columns to preserve from the right DataFrame.
    • Defaults to right_on.
  • method : str or list, default 'exact'
    • Perform a fuzzy match, and an optional specified algorithm.
    • Multiple algorithms can be specified which will apply to each field respectively.
    • Options:
      • exact: exact matches
      • levenshtein: string distance metric
      • jaro: string distance metric
      • metaphone: phoenetic matching algorithm
      • bilenko: prompts for matches
  • threshold : float or list, default 0.6
    • The threshold for a fuzzy match as a number between 0 and 1. Multiple numbers will be applied to each field respectively.
  • ignore_case : bool, default False
    • Ignore case (default is case-sensitive)
  • ignore_nonalpha : bool, default False
    • Ignore non-alphanumeric characters
  • ignore_nonlatin : bool, default False
    • Ignore characters from non-latin alphabets. Accented characters are compared to their unaccented equivalent
  • ignore_order_words : bool, default False
    • Ignore the order words are given in
  • ignore_order_letters : bool, default False
    • Ignore the order the letters are given in, regardless of word order
  • ignore_titles : bool, default False
    • Ignore a predefined list of name titles (such as Mr, Ms, etc)
  • join : { 'inner', 'left-outer', 'right-outer', 'full-outer' }

For more how-to information, check out [the examples folder](https://github.com/jsoma/fuzzy_pandas/tree/master/examples) or the [the original repo](https://github.com/maxharlow/csvmatch).

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