Metadata-Version: 2.1
Name: take-satisfaction
Version: 1.0.0
Summary: Generate a rate between 0 and 1 for Consumer Satisfaction Survey.
Home-page: UNKNOWN
Author: squad XD
Author-email: anaytics.ped@take.net
Maintainer: Take - D&A
Maintainer-email: anaytics.ped@take.net
License: MIT License
Keywords: BLiP,score,satisfaction
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3.7
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Requires-Dist: emoji (==0.6.0)
Requires-Dist: fuzzywuzzy (==0.18.0)
Requires-Dist: numpy (==1.19.4)
Requires-Dist: pandas (==1.1.4)
Requires-Dist: python-dateutil (==2.8.1)
Requires-Dist: pytz (==2020.4)
Requires-Dist: six (==1.15.0)
Requires-Dist: Unidecode (==1.1.1)
Requires-Dist: python-Levenshtein (==0.12.0)

# Take Satisfaction
This package proposes to offer a rate that represents the customer satisfaction research from the bot.

The proposal converts the Customer Satifaction Survey (CSS) to a normalized rate between 0 to 1. The normalized value alow the comparasion of CSS from differents bot that have differents scales types and ranges.

# Installation
Use [pip](https://pypi.org/project/take-satisfaction/) to install:

```shell script
pip install take-satisfaction
```

# Usage

## Using a **numeric scale** Consumer Satisfaction Survey:

```python
import pandas as pd
import take_satisfaction as ts

pdf = pd.DataFrame({"Action": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
                    "amount": [0, 1, 2, 0, 0, 10, 0, 35, 200, 360, 3330]})

result = ts.run(dataframe=pdf,
                scale_column="Action",
                amount_column="amount")

print(result["rate"])
```

Which will result in `0.9761300152361605`.

## Using a **textual scale** Consumer Satisfaction Survey:

```python
import pandas as pd
import take_satisfaction as ts

pdf = pd.DataFrame(
    {"Action": ["PÃ©ssimo", "Ruim", "OK", "Ã“timo", "Excelente"],
    "amount": [0, 1, 35, 350, 3330]})
css_column = "Action"
amount = "amount"

result = ts.run(dataframe=pdf,
                scale_column=css_column,
                amount_column=amount)

print(result["rate"])
```

Which will result in `0.9715419806243273`.


# Author
Take Data&Analytics Research - squad XD.


