semvec/pypi-packages huggingface dataset:
take-resolution 1.0.0
Generate a rate between 0 and 1 for Resolution rate on smart contacts.
License: MIT License
Requires Python: >=3.6
Keywords: BLiP,score,resolution

# Take Resolution

This package proposes to offer a rate that represents the user resolution on the smart contact. 

The proposal considers that users have resolution every time they receive a service provided by the smart contact. The rate is a value between 0 and 1 and as closer to 1, the better the rate is.

If the metric is:
  * 1: all users receive had at least one resolution on the smart contact
  * 0: all users didn't had at least one resolution on the smart contact

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

```shell script
pip install take-resolution
```

# Usage

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

```python
import pandas as pd
import take_resolution as tr

df = pd.DataFrame(
    {"amount": [11198, 11310, 11164, 10571, 11277, 7903, 4968],
    "DAUs": [13487, 13859, 12979, 12342, 17030, 9631, 6099]})

result = tr.run(dataframe=pd,
                amount="amount",
                dau_column="DAUs")

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

Which will result in `0.7665976833542061`.

# Author
Take Data&Analytics Research - squad XD.
take-satisfaction 1.0.0
Generate a rate between 0 and 1 for Consumer Satisfaction Survey.
License: MIT License
Requires Python: >=3.6
Keywords: BLiP,score,satisfaction

# 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.