> For the complete documentation index, see [llms.txt](https://docs.auditboardanalytics.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.auditboardanalytics.com/tools/code/pythoncode/fuzzy-match.md).

# Fuzzy Match

One common use case to utilize the python tool for, is fuzzy matching. Fuzzy matching is a process that finds strings in a dataset that are approximately similar to a target string, even if they aren't an exact match, by using algorithms to calculate the degree of similarity.

### Input, Output

| Input                                                                           | Output                                                                               |
| ------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------ |
| A table from an Analytics tool output with two columns able to be fuzzy matched | Your python altered data frames or charts with similarity score dictated by `return` |

### Example Use Cases

* Matching address that may have been entered incorrectly or slightly different 
* Matching names they may have been entered incorrectly or slightly different
* Assistance in reconciling data between different systems where exact matches aren't guaranteed

### Example Input Table

| A      | B    |
| ------ | ---- |
| Apple  | grpe |
| Banana | appl |
| Orange | bnna |
| Grape  | orng |

### Example Code

This example code creates a fuzzy match from a tool input and only returns those matches with a similarity score above .5 via `def fuzzy_match(list1, list2, threshold=0.5):`. It matches the first two columns provided, so you will need to rearrange your data to be matched as the first two columns.

```
#import Libraries
import pandas as pd
import difflib

# Function to perform fuzzy matching
def fuzzy_match(list1, list2, threshold=0.5):  # Lowering the threshold to 0.5
    matches = []
    for item in list1:
        match = difflib.get_close_matches(item, list2, n=1, cutoff=threshold)
        if match:
            similarity = difflib.SequenceMatcher(None, item, match[0]).ratio() * 100
            matches.append((item, match[0], similarity))
    return matches


#Convert list of dictionaries to pandas dataframe
tbl = sources[0]
df = tbl.df 


#Print the Dataframe to check structure
print("DataFrame structure:\n",df)


#Select the first two columns dynamically
list1 = df.iloc[:,0].tolist()
list2 = df.iloc[:,1].tolist()

#Perform Fuzzy Matching
matches = fuzzy_match(list1, list2)


#Create Dataframe for reults
results_df = pd.DataFrame(matches, columns=['Original Column','Match','Similarity Score'])


#Print and Return Results
print("\nResults:\n",results_df)
return [Table(df=results_df, name="Fuzzy Match Results")]
```

### Example Output Table

| Original Column | Match | Similarity Score |
| --------------- | ----- | ---------------- |
| Apple           | appl  | 66.67            |
| Orange          | orng  | 60.00            |
| Grape           | grpe  | 66.67            |


---

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