lifestream 0.0.7

The fastest way to make sense of a transaction log.

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 to install foobar.

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

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.

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.

Want to plot sales by month?

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.

lifestream.cohort_retention_chart(df, date_col, 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.

Plot how many new users you are acquiring per month.

lifestream.new_customers_chart(df, date_col, user_id)

• 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.

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