field-match 0.1.0

Identify corresponding fields between datasets

field-match

Overview

field-match is a Python library designed to analyze and compare fields between two datasets based on similarity scores, and match up the field names.

Installation

To install the package, run:

pip install field-match

Usage

Import and use the package as follows:

import pandas as pd
from field_match import field_similarity_report, generate_column_rename

# Load your datasets
df1 = pd.read_csv('dataset1.csv')
df2 = pd.read_csv('dataset2.csv')

# Use the field_similarity_report function to match up fields by similarity score
results = field_similarity_report(df1, df2)
                   
# Generate the column rename code snippet
rename_snippet = generate_column_rename(results)                      

For more detailed examples, please refer to the examples folder in this repository.

Example Applications

1. Clarifying Ambiguous Dataset Fields: When merging or integrating a new or updated dataset (such as data from a another year or source) with an existing dataset or workflow, uncertainty in how fields correspond to each may exist because of a lack of headers or differing field names. field_match can help identify which fields in the new dataset correspond to expected fields.

2. Integrating External Dataset with Existing Model: When feeding an external dataset into a pre-existing model, it's crucial to ensure that the data aligns correctly with the model's expected input format. field_match can help you identify which fields in your new dataset correspond to the fields your model expects.

License

field-match is released under the MIT License.