textpackf 0.0.6
Quickly identify and group similar text strings in a large dataset

## What is this?

TextPack efficiently groups similar values in large (or small) datasets. Under the hood, it builds a document term matrix of n-grams assigned a TF-IDF score. It then uses matrix multiplication to calculate the cosine similarity between these values. For a technical explination, [I wrote a blog post](https://medium.com/p/2493b3ce6d8d).

## Why do I care?

If you're a analyst, journalist, data scientist or similar and have ever had a spreadsheet, SQL table or JSON string filled with inconsistent inputs like this:

| row |     fullname      |
|-----|-------------------|
|   1 | John F. Doe       |
|   2 | Esquivel, Mara    |
|   3 | Doe, John F       |
|   4 | Whyte, Luke       |
|   5 | Doe, John Francis |

And you want to perform some kind of analysis – perhaps in a Pivot Table or a Group By statement – but are hindered by the deviations in spelling and formatting, you can use TextPack to comb thousands of cells in seconds and create a third column like this:

| row |     fullname      |  name_groups  |
|-----|-------------------|---------------|
|   1 | John F. Doe       | Doe John F    |
|   2 | Esquivel, Mara    | Esquivel Mara |
|   3 | Doe, John F       | Doe John F    |
|   4 | Whyte, Luke       | Whyte Luke    |
|   5 | Doe, John Francis | Doe John F    |

We can then group by `name_groups` and perform our analysis. 

You can also group across multiple columns. For instance, given the following:

| row |  make  |   model   |
|-----|--------|-----------|
|   1 | Toyota | Camry     |
|   2 | toyta  | camry DXV |
|   3 | Ford   | F-150     |
|   4 | Toyota | Tundra    |
|   5 | Honda  | Accord    |

You can group across `make` and `model` to create:

| row |  make  |   model   |  car_groups  |
|-----|--------|-----------|--------------|
|   1 | Toyota | Camry     | toyotacamry  |
|   2 | toyta  | camry DXV | toyotacamry  |
|   3 | Ford   | F-150     | fordf150     |
|   4 | Toyota | Tundra    | toyotatundra |
|   5 | Honda  | Accord    | hondaaccord  |

## How do I use it?

#### Installation

```
pip install textpack
```

#### Import module

```
from textpack import tp
```

#### Instantiate TextPack

```
tp.Textpack(df, columns_to_group, match_threshold=0.75, ngram_remove=r'[,-./]', ngram_length=3)
```

Class parameters:

 - `df` (required): A Pandas' DataFrame containing the dataset to group
 - `columns_to_group` (required): A list or string matching the column header(s) you'd like to parse and group
 - `match_threshold` (optional): This is a floating point number between 0 and 1 that represents the cosine similarity threshold we'll use to determine if two strings should be grouped. The closer the threshold to 1, the higher the similarity will need to be to be considered a match.
 - `ngram_remove` (optional): A regular expression you can use to filter characters out of your strings when we build our n-grams.
 - `ngram_length` (optional): The length of our n-grams. This can be used in tandem with `match_threshold` to find the sweet spot for grouping your dataset. If TextPack is running slow, it's usually a sign to consider raising the n-gram length.

TextPack can also be instantiated using the following helpers, each of which is just a wrapper that converts a data format to a Pandas DataFrame and then passes it to TextPack. Thus, they all require a file path, `columns_to_group` and take the same three optional parameters as calling `TextPack` directly.

```
tp.read_csv(csv_path, columns_to_group, match_threshold=0.75, ngram_remove=r'[,-./]', ngram_length=3)
```

```
tp.read_excel(excel_path, columns_to_group, sheet_name=None, match_threshold=0.75, ngram_remove=r'[,-./]', ngram_length=3)
```

```
tp.read_json(json_path, columns_to_group, match_threshold=0.75, ngram_remove=r'[,-./]', ngram_length=3)
```

#### Run Textpack and group values

TextPack objects have the following public properties:

 - `df`: The dataframe used internally by TextPack – manipulate as you see fit
 - `group_lookup`: A Python dictionary built by `build_group_lookup` and then used by `add_grouped_column_to_data` to lookup each value that has a group. It looks like this: 

```
{ 
    'John F. Doe': 'Doe John F',
    'Doe, John F': 'Doe John F',
    'Doe, John Francis': 'Doe John F'
}
```

Textpack objects also have the following public methods:

 - `build_group_lookup()`: Runs the cosine similarity analysis and builds `group_lookup`.
 - `add_grouped_column_to_data(column_name='Group')`: Uses vectorization to map values to groups via `group_lookup` and add the new column to the DataFrame. The column header can be set via `column_name`.
 - `set_match_threshold(match_threshold)`: Modify the match threshold internally.
 - `set_ngram_remove(ngram_remove)`: Modify the n-gram regex filter internally.
 - `set_ngram_length(ngram_length)`: Modify the n-gram length internally.
 - `run(column_name='Group')`: A helper function that calls `build_group_lookup` followed by `add_grouped_column_to_data`.

 #### Export our grouped dataset

  - `export_json(export_path)`
  - `export_csv(export_path)`

#### A simple example

```
from textpack import tp

cars = tp.read_csv('./cars.csv', ['make', 'model'], match_threshold=0.8, ngram_length=5)

cars.run()

cars.export_csv('./cars-grouped.csv')
```

## How does it work?

As mentioned above, under the hood, we're building a document term matrix of n-grams assigned a TF-IDF score. We're then using matrix multipcation to quickly calculate the cosine similarity between these values.

I wrote [this blog post](https://medium.com/p/2493b3ce6d8d) to explian how TextPack works behind the scene. Check it out!