Metadata-Version: 2.1
Name: lifestream
Version: 0.0.5
Summary: The fastest way to make sense of a transaction log.
Home-page: https://github.com/invictus2010/lifestream
Author: Jeff Withington
Author-email: jeffrey.withington@gmail.com
License: UNKNOWN
Description: # Lifestream
        
        Lifestream is a Python library to make sense out of your transaction logs. Import a log of your transactional data and let's explore! 
        
        ## Installation
        
        Use the package manager [pip](https://pip.pypa.io/en/stable/) to install foobar.
        
        ```bash
        pip install lifestream
        ```
        ## Transactional Data 
        At a minimum, the transactional data you import should have the following: 
        
        * OrderID assoiated with transaction
        * Unique user id associated with transaction
        * Date of transaction
        * Monetary value of transaction
        
        
        | order_id | user_id | date       | monetary_value |
        |----------|---------|------------|----------------|
        | 768      | 13      | 09/13/2020 | $15.12        |
        | 769      | 13249   | 09/13/2020 | $240.00        |
        | 770      | 11424   | 09/13/2020 | $194.34        |
        
        *Is your transactional data in another kind of format? See the `create_transaction_log` function below.*
        
        ## Usage
        Want to plot sales by month?
        ```python
        import lifestream
        
        lifestream.sales_chart(transaction_log, date_col, monetary_val, user_id)
        ```
        * **transaction_log** is a dataframe of your transactional data.
        * **date_col** represents the column of the transaction_log dataframe which contains the datetime of the transaction.
        * **monetary_val** represents the column of the transaction_log dataframe which contains the monetary value of the transaction. 
        * **user_id** represents the column of the transaction_log dataframe which contains the unique user id associated with the transaction. 
        
        Want to dig into basic cohort analyses? Plot how many users from a cohort are still spending in subsequent months.
        ```python
        
        lifestream.cohort_retention_chart(df, date_col, order_id, user_id, monetary_val, cohort1, cohort2, cohort3)
        ```
        * **df** is a dataframe of your transactional data.
        * **date_col** represents the column of the dataframe which contains the datetime of the transaction.
        * **user_id** represents the column of the dataframe which contains the unique user id associated with the transaction. 
        * **monetary_val** represents the column of the dataframe which contains the monetary value of the transaction. 
        * **cohort1**, **cohort2**, **cohort3** are the three cohorts you are interested in, expressed as 'YYYY-MM' string.
        
        Need to create a transaction log that meets the library's requirements? If your data is as raw as the individually purchased items, try this method.
        ```python
        
        lifestream.create_transaction_log(df, invoicenum, date_col, quantity, unitprice, customerid)
        ```
        * **df** is a dataframe of your  data.
        * **date_col** represents the column of the dataframe which contains the datetime of the transaction.
        * **user_id** represents the column of the dataframe which contains the unique user id associated with the transaction. 
        * **quantity** represents the column of the dataframe which contains the quantity of an item purchased in the transaction.
        * **unitprice** represents the column of the dataframe which contains the price of an item purchased in the transaction
        * **customerid** is the unique id associated with the customer making the purchase.
        
        ## Contributing
        Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.
        
        Please make sure to update tests as appropriate.
        
        ## License
        [MIT](https://choosealicense.com/licenses/mit/)
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Classifier: Intended Audience :: Information Technology
Classifier: Intended Audience :: Financial and Insurance Industry
Classifier: License :: OSI Approved :: MIT License
Classifier: Topic :: Office/Business
Classifier: Topic :: Office/Business :: Financial
Classifier: Topic :: Internet :: Log Analysis
Description-Content-Type: text/markdown
