Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

https://github.com/Lyonk71/pandas-dedupe

Simplifies use of the Dedupe library via Pandas
https://github.com/Lyonk71/pandas-dedupe

Last synced: 15 days ago
JSON representation

Simplifies use of the Dedupe library via Pandas

Lists

README 

        
# 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



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



```python

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')

```



### Gazetteer deduplication (gazetteer_dataframe)

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



```python

import pandas as pd

import pandas_dedupe



#load dataframe

df_clean = pd.read_csv('gazette.csv')

df_messy = pd.read_csv('test_names.csv')



#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')

```



### Matching / Record Linkage



Use identical field names when linking dataframes.

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



```python

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')

```



# Advanced Usage



### Canonicalize Fields



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



```python

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.



```python

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.



```python

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

```



### Update 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.

```python

pandas_dedupe.dedupe_dataframe(df, ['first_name', 'last_name'], sample_size=0.5)

```



### 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](#Types).



```python

# 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.](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, 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.



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`, added support for multiprocessing, added `gazetteer_dataframe`.



[Daniel Marczin](https://github.com/dim5) - Extensive updates to documentation to enhance readability.



[Alexis-Evelyn](https://github.com/alexis-evelyn) - Fixed logger warning with related to Pandas.



[Niels Horn](https://github.com/nilq) - Cleaned up utility functions.



# 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/).