Principles of Record Linkage
Record linkage is a crucial process in data integration that involves identifying and connecting records from different datasets that refer to the same real-world entity. This technique is essential when you need to merge information from multiple sources, such as linking patient records from different hospitals, consolidating customer databases after a merger, or integrating product catalogs from various vendors. The primary goal is to accurately match equivalent records, even when there are discrepancies in data entry, formatting, or missing values.
1234567891011121314151617181920212223242526272829303132import pandas as pd # DataFrames from two sources df_a = pd.DataFrame({ "first_name": ["Alice", "Bob", "Charlie"], "last_name": ["Smith", "Jones", "Brown"], "dob": ["1990-01-01", "1985-05-12", "1975-09-23"], "city": ["New York", "Los Angeles", "Chicago"] }) df_b = pd.DataFrame({ "first_name": ["Alice", "Robert", "Charles"], "last_name": ["Smith", "Jones", "Brown"], "dob": ["1990-01-01", "1985-05-12", "1975-09-23"], "city": ["New York", "Los Angeles", "Chicago"] }) # Composite key: (first_name, last_name, dob) df_a["composite_key"] = ( df_a["first_name"].str.lower() + "_" + df_a["last_name"].str.lower() + "_" + df_a["dob"] ) df_b["composite_key"] = ( df_b["first_name"].str.lower() + "_" + df_b["last_name"].str.lower() + "_" + df_b["dob"] ) # Find matching records based on composite key matches = pd.merge(df_a, df_b, on="composite_key", suffixes=('_a', '_b')) print(matches[["first_name_a", "last_name_a", "dob", "city_a", "city_b"]])
Blocking is a technique used in record linkage to reduce the number of comparisons between records by grouping them into smaller blocks based on common attributes. This approach improves efficiency by only comparing records within the same block, rather than across the entire dataset.
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Can you explain how the composite key helps in matching records?
What if the names are slightly different, like "Bob" and "Robert"? How can we handle that?
Are there other techniques for record linkage besides using composite keys?
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Record linkage is a crucial process in data integration that involves identifying and connecting records from different datasets that refer to the same real-world entity. This technique is essential when you need to merge information from multiple sources, such as linking patient records from different hospitals, consolidating customer databases after a merger, or integrating product catalogs from various vendors. The primary goal is to accurately match equivalent records, even when there are discrepancies in data entry, formatting, or missing values.
1234567891011121314151617181920212223242526272829303132import pandas as pd # DataFrames from two sources df_a = pd.DataFrame({ "first_name": ["Alice", "Bob", "Charlie"], "last_name": ["Smith", "Jones", "Brown"], "dob": ["1990-01-01", "1985-05-12", "1975-09-23"], "city": ["New York", "Los Angeles", "Chicago"] }) df_b = pd.DataFrame({ "first_name": ["Alice", "Robert", "Charles"], "last_name": ["Smith", "Jones", "Brown"], "dob": ["1990-01-01", "1985-05-12", "1975-09-23"], "city": ["New York", "Los Angeles", "Chicago"] }) # Composite key: (first_name, last_name, dob) df_a["composite_key"] = ( df_a["first_name"].str.lower() + "_" + df_a["last_name"].str.lower() + "_" + df_a["dob"] ) df_b["composite_key"] = ( df_b["first_name"].str.lower() + "_" + df_b["last_name"].str.lower() + "_" + df_b["dob"] ) # Find matching records based on composite key matches = pd.merge(df_a, df_b, on="composite_key", suffixes=('_a', '_b')) print(matches[["first_name_a", "last_name_a", "dob", "city_a", "city_b"]])
Blocking is a technique used in record linkage to reduce the number of comparisons between records by grouping them into smaller blocks based on common attributes. This approach improves efficiency by only comparing records within the same block, rather than across the entire dataset.
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