lifestream 0.0.11

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 lifestream.

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, orderid_col, datetime_col customerid_col, quantity_col, 
unitprice_col)

• df is a dataframe of your data.
• orderid_col the column in df DataFrame that denotes the unique order_id.
• datetime_col the column in df DataFrame that denotes the datetime the purchase was made.
• customerid_col the column in df DataFrame that denotes the unique customer_id.
• quantity_col the column in df DataFrame that denotes the quantity of items purchased in an order.
• unitprice_col the column in df DataFrame that denotes the unit price of items purchased in an order.

Want to plot sales by month?

import lifestream

lifestream.sales_chart(transaction_log, datetime_col, ordervalue_col, customerid_col, title, ylabel)

• transaction_log is a dataframe of your transactional data.
• datetime_col represents the column of the transaction_log dataframe which contains the datetime of the transaction.
• orderid_col represents the column of the transaction_log dataframe which contains the monetary value of the transaction.
• customerid_col represents the column of the transaction_log dataframe which contains the unique user id associated with the transaction.
• title optional represents the title of the chart.
• ylabel optional represents the label on the y-axis of the chart.

Want to dig into basic cohort analyses? Plot how many users from a cohort are still spending in subsequent months.

lifestream.cohort_retention_chart(transaction_log, datetime_col, customerid_col, ordervalue_col, cohort1, cohort2, cohort3, title, ylabel)

• transaction_log is a dataframe of your transactional data.
• datetime_col represents the column of the dataframe which contains the datetime of the transaction.
• customerid_col represents the column of the dataframe which contains the unique user id associated with the transaction.
• ordervalue_col 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.
• title optional is the title for the plot.
• ylabel optional is the label for the y-axis of the plot.

Plot how many new users you are acquiring per month.

lifestream.new_customers_chart(transaction_log, datetime_col, customerid_col, title, xlabel, ylabel, kind)

• transaction_log is a dataframe of your transactional data.
• datetime_col represents the column of the dataframe which contains the datetime of the transaction.
• customerid_col represents the column of the dataframe which contains the unique user id associated with the transaction.
• title optional represents the title of the chart.
• xlabel optional represents the x-axis of the chart.
• ylabel optional represents the y-axis of the chart.
• kind optional represents the kind of chart. see the pandas library documentation for the plot method to understand what is available.

Plot the monthly revenue mix by new vs. returning customers.

lifestream.customer_type_revenue_mix(transaction_log, datetime_col, customerid_col, ordervalue_col, figsize = (12,8), rotation = 'vertical'
)

• transaction_log is a dataframe of your transactional data.
• datetime_col represents the column of the dataframe which contains the datetime of the transaction.
• customerid_col represents the column of the dataframe which contains the unique user id associated with the transaction.
• ordervalue_col represents the column of the dataframe which contains the monetary value of the transaction.
• figsize optional represents the size of the chart.
• rotation optional represents the rotation of the x-axis tick marks on the chart.

Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

License

MIT