semvec/pypi-packages huggingface dataset:
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watch-the-conjuring-3-2021-full-online-free-hd-quality 2.0.0
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cfeventlogging 1.0.1
Custom Logging Package for entry, exit and error event logging
Requires Python: >=3.6

## eventlogging

A sample python package deployment utility for custom logging. It has functions for entry, exit and error logging. It takes the input dictionary (if present) and the logging entry type.
If no input dictionary is present pass None. Output will be a JSON format logged printed as below. Comes in handy for cloud functions

{"TestEVENT": "EXIT", "status": "SUCCESS", "statusCode": "0000"}

{"TestEVENT": "EXIT", "status": "SUCCESS", "statusCode": "0000", "entryType": "insertUser", "opName": "insertUser", "src": "Cloud_Functions", "action": "Insert User", "respTime": "1 secs"}

{"TestEVENT": "ERROR", "status": "FAILURE", "statusCode": "9999"}


## Usage

import cfeventlogging

dict = {"entryType": "insertUser", "opName": "insertUser", "src": "Cloud_Functions", "action": "Insert User"}

eventlogging.entryEventLog(dict, "TestEVENT")

eventlogging.exitEventLog(dict, "TestEVENT")

eventlogging.errorEventLog(dict, "TestEVENT")
retenmod 0.0.1
retention models to forecast churn
Requires Python: >=3.6

# retenmod

This is a python port of the R package [foretell](https://cran.r-project.org/web/packages/foretell/foretell.pdf). This projects customer retention rates, see [*“How to Project Customer Retention” Revisited: The Role of Duration Dependence* (Fader et al., 2018)](https://www.sciencedirect.com/science/article/pii/S1094996818300057) for the original formulation and description of the models. 

To install, just use pip:

    pip install retenmod

Only dependencies are scipy and numpy. For a simple example of use for the BdW model:

    import retenmod
    surv = [100, 86.9, 74.3, 65.3, 59.3]
    res = retenmod.bdw(surv,6)
    print(res.params)
    print(res.proj)