The Dedupe library made easy with Pandas.

# pandas-dedupe

The Dedupe library made easy with Pandas.

# Installation

pip install pandas-dedupe


# Video Tutorials

Basic Deduplication

# Basic Usage

A training file and a settings file will be created while running Dedupe. Keeping these files will eliminate the need to retrain your model in the future.

If you would like to retrain your model from scratch, just delete the settings and training files.

### Deduplication (dedupe_dataframe)

dedupe_dataframe is for deduplication when you have data that can contain multiple records that can all refer to the same entity

import pandas as pd
import pandas_dedupe

#initiate deduplication
df_final = pandas_dedupe.dedupe_dataframe(df,['first_name', 'last_name', 'middle_initial'])

#send output to csv
df_final.to_csv('deduplication_output.csv')


### Gazetteer deduplication (gazetteer_dataframe)

gazetteer_dataframe is for matching a messy dataset against a 'canonical dataset' (i.e. the gazette)

import pandas as pd
import pandas_dedupe

#initiate deduplication
df_final = pandas_dedupe.gazetteer_dataframe(df_clean, df_messy, 'fullname', canonicalize=True)

#send output to csv
df_final.to_csv('gazetteer_deduplication_output.csv')


Use identical field names when linking dataframes. Record linkage should only be used on dataframes that have been deduplicated.

import pandas as pd
import pandas_dedupe

#initiate matching
df_final = pandas_dedupe.link_dataframes(dfa, dfb, ['field_1', 'field_2', 'field_3', 'field_4'])

#send output to csv


### Canonicalize Fields

The canonicalize parameter will standardize names in a given cluster. Original fields are also kept.

pandas_dedupe.dedupe_dataframe(df,['first_name', 'last_name', 'payment_type'], canonicalize=True)


### Update Threshold (dedupe_dataframe and gazetteer_dataframe only)

Group records into clusters only if the cophenetic similarity of the cluster is greater than the threshold.

pandas_dedupe.dedupe_dataframe(df, ['first_name', 'last_name'], threshold=.7)


### Update Existing Model (dedupe_dataframe and gazetteer_dataframe only)

If True, it allows a user to update the existing model.

pandas_dedupe.dedupe_dataframe(df, ['first_name', 'last_name'], update_model=True)


### Recall Weight & Sample Size

The dedupe_dataframe() function has two optional parameters specifying recall_weight and sample_size:

• recall_weight - Ranges from 0 to 2. When set to 2, we are saying we care twice as much about recall than we do about precision.
• sample_size - Specifies the sample size used for training as a float from 0 to 1. By default it is 30% (0.3) of our data.

### Specifying Types

If you'd like to specify dates, spatial data, etc, do so here. The structure must be like so: ('field', 'type', 'additional_parameter). the additional_parameter section can be omitted. The default type is String.

See the full list of types below.

# Price Example
pandas_dedupe.dedupe_dataframe(df,['first_name', 'last_name', ('salary', 'Price')])

# has missing Example
pandas_dedupe.link_dataframes(df,['SSN', ('bio_pgraph', 'Text'), ('salary', 'Price', 'has missing')])

# crf Example
pandas_dedupe.dedupe_dataframe(df,[('first_name', 'String', 'crf'), 'last_name', (m_initial, 'Exact')])


# Types

Dedupe supports a variety of datatypes; a full list with documentation can be found here.

pandas-dedupe officially supports the following datatypes:

• String - Standard string comparison using string distance metric. This is the default type.
• Text - Comparison for sentences or paragraphs of text. Uses cosine similarity metric.
• Price - For comparing positive, non zero numerical values.
• DateTime - For comparing dates.
• LatLong - (39.990334, 70.012) will not match to (40.01, 69.98) using a string distance metric, even though the points are in a geographically similar location. The LatLong type resolves this by calculating the haversine distance between compared coordinates. LatLong requires the field to be in the format (Lat, Long). The value can be a string, a tuple containing two strings, a tuple containing two floats, or a tuple containing two integers. If the format is not able to be processed, you will get a traceback.
• Exact - Tests whether fields are an exact match.
• Exists - Sometimes, the presence or absence of data can be useful in predicting a match. The Exists type tests for whether both, one, or neither of fields are null.

• has missing - Can be used if one of your data fields contains null values
• crf - Use conditional random fields for comparisons rather than distance metric. May be more accurate in some cases, but runs much slower. Works with String and ShortString types.

# Contributors

Tyler Marrs - Refactored code, added docstrings, added threshold parameter

Tawni Marrs - refactored code, added docstrings

ieriii - Added update_model parameter, updated codebase to use Dedupe 2.0, added support for multiprocessing, added gazetteer_dataframe.

# Credits

Many thanks to folks at DataMade for making the the Dedupe library publicly available. People interested in a code-free implementation of the dedupe library can find a link here: Dedupe.io.

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