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Description:

lifestream 0.0.18

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
This library is inspired by many of the charts found in this PowerPoint file created by Prof Daniel McCarthy.
Below are some of the methods found within this library. Not all methods are in the readme, so open-up the lifestream.py file for some Easter eggs.
Transaction Log Creation
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.

Monthly Sales Chart
Want to plot sales by month?
import lifestream

lifestream.sales_chart(transaction_log, datetime_col, ordervalue_col, customerid_col, customer_count = True, title = 'Sales and Customers Per Month', ylabel1 = 'Number of Customers Per Month', ylabel2 = 'Sales ($) per Month')

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.
customer_count optional boolean to indicate whether overlay of new customers per month is desired
title optional represents the title of the chart.
ylabel1 optional represents the label on the y-axis of the line chart.
ylabel2 optional represents the label on the y-axis of the bar chart.

Cohort Retention 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.

Monthly Acquisition Chart
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.

The C3 Chart 🤩
lifestream.c3chart(transaction_log, customer_id, datetime_col, ordervalue_col, title="Total Quarterly Sales by Acquisition Cohort Over Time")

transaction_log is a dataframe of your transactional data.
customerid_col represents the column of the dataframe which contains the unique user id associated with the transaction.
datetime_col represents the column of the dataframe which contains the datetime of the transaction.
ordervalue_col represents the column of the dataframe which contains the monetary value of the transaction.
title optional represents the title of 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