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The Dedupe library made easy with Pandas.

Project description

# pandas-dedupe The Dedupe library made easy with Pandas.

# Installation

pip install pandas-dedupe

# Video Tutorials

[Basic Deduplication](https://www.youtube.com/watch?v=lCFEzRaqoJA)

# Basic Usage

### Deduplication

import pandas as pd import pandas_dedupe

#load dataframe df = pd.read_csv(‘test_names.csv’)

#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’)

#——————————additional details——————————

#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, just delete the settings and training files.

### Matching / Record Linkage

import pandas as pd import pandas_dedupe

#load dataframes dfa = pd.read_csv(‘file_a.csv’) dfb = pd.read_csv(‘file_b.csv’)

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

#send output to csv df_final.to_csv(‘linkage_output.csv’)

#——————————additional details——————————

#Use identical field names when linking dataframes.

#Record linkage should only be used on dataframes that have been deduplicated.

#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, just delete the settings and training files.

# Advanced Usage

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

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

### Update Threshold (dedupe_dataframe only) Only put together records into clusters 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 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

Within the dedupe_dataframe() function, optional parameters exist for specifying recall_weight and sample_size: * recall_weight - Ranges from 0 to 2. When we set a recall weight of 2, we are saying we care twice as much about recall as we do precision * sample_size - Specify 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 ommitted. 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 listing with documentation can be found [here.](https://docs.dedupe.io/en/latest/Variable-definition.html#)

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, Lng). 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 wheter 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.

Additional supported parameters are: * 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](http://tylermarrs.com/) - Refactored code, added docstrings, added threshold parameter

[Tawni Marrs](https://github.com/tawnimarrs) - refactored code, added docstrings

[ieriii](https://github.com/ieriii) - Added update_model parameter, updated codebase to use Dedupe 2.0

# Credits

Many thanks to folks at [DataMade](https://datamade.us/) for making the the [Dedupe library](https://github.com/dedupeio/dedupe) publicly available. People interested in a code-free implementation of the dedupe library can find a link here: [Dedupe.io](https://dedupe.io/pricing/).

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