Learn Implementing Record Linkage in Python

To implement record linkage in Python, you will typically use pandas for data manipulation and comparison. The process starts with loading both datasets into DataFrames, followed by selecting the fields (columns) that are most relevant for linking. You may need to preprocess these fields—such as trimming whitespace, converting to lowercase, or removing punctuation—to improve matching accuracy. After preprocessing, you can create a composite key by combining multiple fields. This key helps identify potential matches between records, even when there are slight variations in the data. For more advanced matching, fuzzy string matching can be applied to account for typos or formatting differences. Finally, you compare the records from both datasets using these keys, identify links, and analyze the results to ensure the matches are accurate and meaningful.


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import pandas as pd
from difflib import SequenceMatcher

def link_records(df1, df2, key_fields, threshold=0.85):
    def make_key(row):
        return " ".join(str(row[field]).strip().lower() for field in key_fields)
    
    df1 = df1.copy()
    df2 = df2.copy()
    df1["composite_key"] = df1.apply(make_key, axis=1)
    df2["composite_key"] = df2.apply(make_key, axis=1)
    
    matches = []
    for idx1, row1 in df1.iterrows():
        for idx2, row2 in df2.iterrows():
            score = SequenceMatcher(None, row1["composite_key"], row2["composite_key"]).ratio()
            if score >= threshold:
                matches.append({
                    "df1_index": idx1,
                    "df2_index": idx2,
                    "df1_key": row1["composite_key"],
                    "df2_key": row2["composite_key"],
                    "similarity": score
                })
    return pd.DataFrame(matches)


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# Customer file from system A
df_a = pd.DataFrame({
    "first_name": ["Alice", "Bob", "Charlie"],
    "last_name": ["Johnson", "Smith", "Reed"],
    "city": ["New York", "Boston", "Seattle"]
})

# Customer file from external system B
df_b = pd.DataFrame({
    "first_name": ["Alicia", "Robert", "Charley"],
    "last_name": ["Jonson", "Smyth", "Reid"],
    "city": ["NewYork", "Bostn", "Seatle"]
})

# Selecting key fields to build a composite matching key
key_fields = ["first_name", "last_name", "city"]

# Running fuzzy record linkage
linked = link_records(df_a, df_b, key_fields, threshold=0.80)

print(linked)

Mismatched Data Types

Make sure all fields used for matching are of string type to avoid errors during comparison;

Inconsistent Formatting

Normalize case, remove extra spaces, and strip punctuation for more reliable matching;

Threshold Tuning

The fuzzy matching threshold may need adjustment depending on your data—too high can miss true matches, too low can create false positives;

Performance Issues

Comparing every record to every other record can be slow for large datasets; consider blocking or indexing methods to reduce comparisons;

Overfitting to Sample Data

Always validate your linkage logic on new, unseen data to avoid overfitting to quirks in your current datasets.

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              12345678910111213141516171819202122232425
            
import pandas as pd
from difflib import SequenceMatcher

def link_records(df1, df2, key_fields, threshold=0.85):
    def make_key(row):
        return " ".join(str(row[field]).strip().lower() for field in key_fields)
    
    df1 = df1.copy()
    df2 = df2.copy()
    df1["composite_key"] = df1.apply(make_key, axis=1)
    df2["composite_key"] = df2.apply(make_key, axis=1)
    
    matches = []
    for idx1, row1 in df1.iterrows():
        for idx2, row2 in df2.iterrows():
            score = SequenceMatcher(None, row1["composite_key"], row2["composite_key"]).ratio()
            if score >= threshold:
                matches.append({
                    "df1_index": idx1,
                    "df2_index": idx2,
                    "df1_key": row1["composite_key"],
                    "df2_key": row2["composite_key"],
                    "similarity": score
                })
    return pd.DataFrame(matches)


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# Customer file from system A
df_a = pd.DataFrame({
    "first_name": ["Alice", "Bob", "Charlie"],
    "last_name": ["Johnson", "Smith", "Reed"],
    "city": ["New York", "Boston", "Seattle"]
})

# Customer file from external system B
df_b = pd.DataFrame({
    "first_name": ["Alicia", "Robert", "Charley"],
    "last_name": ["Jonson", "Smyth", "Reid"],
    "city": ["NewYork", "Bostn", "Seatle"]
})

# Selecting key fields to build a composite matching key
key_fields = ["first_name", "last_name", "city"]

# Running fuzzy record linkage
linked = link_records(df_a, df_b, key_fields, threshold=0.80)

print(linked)