Valentine

(Schema-) Matching DataFrames Made Easy

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A Python package for capturing potential relationships among columns of different tabular datasets, given as pandas DataFrames. Valentine is based on the paper Valentine: Evaluating Matching Techniques for Dataset Discovery.

📚 Full documentation: https://delftdata.github.io/valentine/ — getting started, matcher guide, API reference, and migration notes.

Experimental suite version

The original experimental suite version of Valentine, as first published for the needs of the research paper, can be still found here.

Installation instructions

Requirements

• Python >=3.10,<3.15

To install Valentine simply run:

pip install valentine

Usage

Valentine can be used to find matches among columns of a given pair of pandas DataFrames.

Matching methods

In order to do so, the user can choose one of the following matching methods:

1. Coma(int: max_n, bool: use_instances, bool: use_schema, float: delta, float: threshold) is a pure Python implementation of the COMA 3.0 schema matching algorithm.

   • Parameters:
     • max_n(int) - Maximum number of matches to keep per column, 0 means unlimited (default: 0).
     • use_instances(bool) - Whether to use TF-IDF instance-based matching on data values (default: False).
     • use_schema(bool) - Whether to use schema-based matching on column names, paths, and structure (default: True).
     • delta(float) - Fraction from the best score within which matches are kept (default: 0.15).
     • threshold(float) - Absolute minimum similarity score to keep a match (default: 0.0).

2. Cupid(float: w_struct, float: leaf_w_struct, float: th_accept) is the python implementation of the paper Generic Schema Matching with Cupid

   • Parameters:
     • w_struct(float) - Structural similarity threshold, default is 0.2.
     • leaf_w_struct(float) - Structural similarity threshold, leaf level, default is 0.2.
     • th_accept(float) - Accept similarity threshold, default is 0.7.

3. DistributionBased(float: threshold1, float: threshold2) is the python implementation of the paper Automatic Discovery of Attributes in Relational Databases

   • Parameters:
     • threshold1(float) - The threshold for phase 1 of the method, default is 0.15.
     • threshold2(float) - The threshold for phase 2 of the method, default is 0.15.

4. JaccardDistanceMatcher(float: threshold_dist) is a baseline method that uses Jaccard Similarity between columns to assess their correspondence score, optionally enhanced by a string similarity measure of choice.

   • Parameters:

     • threshold_dist(float) - Acceptance threshold for assessing two strings as equal, default is 0.8.

     • distance_fun(StringDistanceFunction) - String similarity function used to assess whether two strings are equal. The enumeration class type StringDistanceFunction can be imported from valentine.algorithms.jaccard_distance. Functions currently supported are:

       • StringDistanceFunction.Levenshtein: Levenshtein distance
       • StringDistanceFunction.DamerauLevenshtein: Damerau-Levenshtein distance
       • StringDistanceFunction.Hamming: Hamming distance
       • StringDistanceFunction.Jaro: Jaro distance
       • StringDistanceFunction.JaroWinkler: Jaro-Winkler distance * StringDistanceFunction.Exact: String equality ==

5. SimilarityFlooding(Policy: coeff_policy, Formula: formula, StringMatcher: string_matcher) is the python implementation of the paper Similarity Flooding: A Versatile Graph Matching Algorithmand its Application to Schema Matching

   • Parameters:
     • coeff_policy(Policy) - Policy for deciding the weight coefficients of the propagation graph. Policy.INVERSE_PRODUCT or Policy.INVERSE_AVERAGE (default).
     • formula(Formula) - Formula on which iterative fixpoint computation is based. Formula.BASIC, Formula.FORMULA_A, Formula.FORMULA_B, or Formula.FORMULA_C (default).
     • string_matcher(StringMatcher) - String matching function for the initial similarity mapping. StringMatcher.PREFIX_SUFFIX (default), StringMatcher.PREFIX_SUFFIX_TFIDF, or StringMatcher.LEVENSHTEIN.

Matching DataFrames

Pass two or more DataFrames as a list (or any iterable) along with a matcher. Valentine will match columns across all unique pairs:

# Match a pair of DataFrames
matches = valentine_match([df1, df2], matcher)

# Match multiple DataFrames (computes all N×(N-1)/2 pairs)
matches = valentine_match([df1, df2, df3], matcher, df_names=["sales", "orders", "products"])

Optionally provide df_names to label each DataFrame (defaults to "aaa", "bbb", etc. — designed to have zero similarity so they don't influence schema-based matchers). Function valentine_match returns a MatcherResults object, an immutable mapping from ColumnPair to similarity scores with convenience methods for filtering, subsetting, and evaluation.

MatcherResults and ColumnPair

Results are keyed by ColumnPair namedtuples with named fields for easy access:

for pair, score in matches.items():
    print(f"{pair.source_column} <-> {pair.target_column}: {score:.3f}")
    # Also available: pair.source_table, pair.target_table
    # Shorthand tuples: pair.source, pair.target

MatcherResults provides convenience methods for filtering and subsetting:

top_n_matches = matches.take_top_n(5)
top_n_percent_matches = matches.take_top_percent(25)
one_to_one_matches = matches.one_to_one()
high_confidence = matches.filter(min_score=0.7)
one_to_one_strict = matches.one_to_one(threshold=0.5)

Match details (Coma)

When using the Coma matcher, per-sub-matcher score breakdowns are available via .details:

for pair, score in matches.items():
    details = matches.get_details(pair)
    if details:
        print(f"{pair.source_column} <-> {pair.target_column}: {details}")
        # e.g. {'NameCM': 0.72, 'PathCM': 0.65, 'LeavesCM': 0.58, ...}

Measuring effectiveness

metrics = matches.get_metrics(ground_truth)

Computes Precision, Recall, F1-score and others as described in the original Valentine paper. The ground truth is a list of (source_column, target_column) tuples:

ground_truth = [("emp_id", "employee_number"), ("fname", "first_name"), ...]

Custom metrics can be specified, and predefined sets are available:

from valentine.metrics import F1Score, PrecisionTopNPercent, METRICS_PRECISION_INCREASING_N
metrics_custom = matches.get_metrics(ground_truth, metrics={F1Score(one_to_one=False), PrecisionTopNPercent(n=70)})
metrics_predefined_set = matches.get_metrics(ground_truth, metrics=METRICS_PRECISION_INCREASING_N)

Example

The following block of code shows: 1) how to run a matcher from Valentine on two DataFrames storing information about job candidates, and then 2) how to assess its effectiveness based on a given ground truth (a more extensive example is shown in valentine_example.py):

import pandas as pd
from valentine import valentine_match
from valentine.algorithms import Coma

# Load data using pandas
df1 = pd.read_csv("source_candidates.csv")
df2 = pd.read_csv("target_candidates.csv")

# Instantiate matcher and run
matcher = Coma(use_instances=True)
matches = valentine_match([df1, df2], matcher)

# Iterate over results using ColumnPair named fields
for pair, score in matches.items():
    print(f"{pair.source_column} <-> {pair.target_column}: {score:.3f}")

# If ground truth available valentine could calculate the metrics
ground_truth = [
    ("emp_id", "employee_number"),
    ("fname", "first_name"),
    ("lname", "last_name"),
    ("dept", "department"),
    ("annual_salary", "compensation"),
    ("hire_date", "start_date"),
    ("office_loc", "work_location"),
]

metrics = matches.get_metrics(ground_truth)
print(metrics)

Cite Valentine

Original Valentine paper:
@inproceedings{koutras2021valentine,
  title={Valentine: Evaluating Matching Techniques for Dataset Discovery},
  author={Koutras, Christos and Siachamis, George and Ionescu, Andra and Psarakis, Kyriakos and Brons, Jerry and Fragkoulis, Marios and Lofi, Christoph and Bonifati, Angela and Katsifodimos, Asterios},
  booktitle={2021 IEEE 37th International Conference on Data Engineering (ICDE)},
  pages={468--479},
  year={2021},
  organization={IEEE}
}
Demo Paper:
@article{koutras2021demo,
  title={Valentine in Action: Matching Tabular Data at Scale},
  author={Koutras, Christos and Psarakis, Kyriakos and Siachamis, George and Ionescu, Andra and Fragkoulis, Marios and Bonifati, Angela and Katsifodimos, Asterios},
  journal={VLDB},
  volume={14},
  number={12},
  pages={2871--2874},
  year={2021},
  publisher={VLDB Endowment}
}