dupandas 0.2

python package to deduplicate text data in pandas dataframe using flexible string matching and cleaning

# **dupandas:** data deduplication of text records in a pandas dataframe


[](https://github.com/shivam5992/dupandas) [](https://twitter.com/shivamshaz)

dupandas is a python package to perform data deduplication on columns of a pandas dataframe using flexible text matching. It is compatible with both versions of python (2.x and 3.x). dupandas can find duplicate any kinds of text records in the pandas data. It comprises of sophisticated Matchers that can handle spelling differences and phonetics. It also comprises of several Cleaners, which can be used to clean up the noise present in the text data such as punctuations, digits, casing etc.

For fast computations, dupandas uses lucene based text indexing. In the input_config, if "indexing" = True, then it indexes the dataset in RAMDirectory which is used to identify and search similar strings. Check out the instructions of installing PyLucene below.

The good part of dupandas is that it's Matchers and Cleaner functions can be used as standalone packages while working with different problems of text data.


## Installation
Following python modules are required to use dupandas: **pandas, fuzzy, python-levenshtein** . These modules can be installed using pip command:

```python
pip install dupandas pandas fuzzy python-levenshtein
```
**OR** if dependencies are already installed:

```
pip install dupandas
```

**OPTIONAL** For faster implementation dupandas with indexing feature is recommended. **PyLucene** Installation : Refer to [this](http://installion.co.uk/ubuntu/vivid/universe/p/pylucene/install/index.html) link.

**Note:** The use of indexing can reduce the overall computation time of completion to one third.

## Usage : dupandas
dupandas using default Matchers and Cleaners, Default Matcher and Cleaners are Exact Match and No Cleaning respectively.

``` python
from dupandas import Dedupe
dupe = Dedupe()

input_config = {
'input_data' : pandas_dataframe,
'column' : 'column_name_to_deduplicate',
'_id' : 'unique_id_column_of_dataset',
}
results = dupe.dedupe(input_config)
```

dupandas using custom Cleaner and Matcher configs

``` python
from dupandas import Dedupe

clean_config = { 'lower' : True, 'punctuation' : True, 'whitespace' : True, 'digit' : True }
match_config = { 'exact' : False, 'levenshtein' : True, 'soundex' : False, 'nysiis' : False}
dupe = Dedupe(clean_config = clean_config, match_config = match_config)

input_config = {
'input_data' : pandas_dataframe,
'column' : 'column_name_to_deduplicate',
'_id' : 'unique_id_column_of_dataset',
}
results = dupe.dedupe(input_config)
```

Other options in input_config

```python
input_config = {
'input_data' : pandas_dataframe,
'column' : 'column_name_to_deduplicate',
'_id' : 'unique_id_column_of_dataset',
'score_column' : 'name_of_the_column_for_confidence_score',
'threshold' : 0.75, # float value of threshold
'unique_pairs' : True, # boolean to get unique (A=B) or duplicate (A=B and B=A) results
'indexing' : False # Boolean to set lucene indexing = True / False, Default: False
}
```

## Usage : standalone Cleaner class

```python
from dupandas import Cleaner
clean_config = { 'lower' : True, 'punctuation' : True, 'whitespace' : True, 'digit' : True }
clean = Cleaner(clean_config)
clean.clean_text("new Delhi 3#! 34 ")
```

## Usage: standalone Matcher class

```python
from dupandas import Matcher
match_config = { 'exact' : False, 'levenshtein' : True, 'soundex' : False, 'nysiis' : False}
match = Matcher(match_config)
match.match_elements("new delhi", "newdeli")
```

## Issues

Thanks for checking this work, Yes ofcourse there is a scope of improvement, Feel free to submit issues and enhancement requests.

## Contributing
#### ToDos

1. [ ] V2: Add Support for multi column match
2. [ ] V2: Add More Matchers, Cleaners
3. [ ] V2: Remove Library Dependencies
4. [ ] V2: Handle Longer Texts, Optimize Speed

#### Steps
1. **Fork** the repo on GitHub
2. **Clone** the project to your own machine
3. **Commit** changes to your own branch
4. **Push** your work back up to your fork
5. Submit a **Pull request**