Additional documentation will be added shortly.

This product includes GeoLite2 data created by MaxMind, available from http://www.maxmind.com.

Creating a Bamboodle object from a Moodle log file CSV:

bb = Bamboodle(pandas.read_csv('Fall2015.csv'))

Adds an integer column for weekday to activity records (0 = Monday, 6 = Sunday):

print bb.add_weekday()

Counts actions taken by weekday (0 = Monday, 6 = Sunday) (groups by user automatically):

print bb.weekday_activity()

Calculates mean weekday of activity records (0 = Monday, 6 = Sunday) (groups by user automatically):

print bb.mean_weekday_activity()

Generates GeoJSON with a point for each unique IP address; contains attributes for activity count:

print bb.ip_locations_geojson()

Retrieves "city, state, country" locations for all users in input (can be pre-filtered by user or any other characteristic, as in this example):

print bb.filter_user('NAME, USER').ip_locations()

Calculates percentage of time that user(s) is/are within the networks passed to this function, then joins grades for a specific assignment to the results (example of a two-step process):

ip_list = ["192.168.0.0/16","10.0.0.0/16"]
print bb.ip_networks(ip_list).load_grades('Fall2015_Grades.csv','assignment3')

Calculates percentage of time that user(s) is/are within the networks passed to this function:

ip_list = ["192.168.0.0/16","10.0.0.0/16"]
print bb.filter_user('NAME, USER').ip_networks(ip_list)

Calculates percentage of time that user(s) is/are within the networks passed to this function, as well as K-means cluster (4 total clusters) for this attribute; grades for assignment are joined also:

ip_list = ["192.168.0.0/16","10.0.0.0/16"]
print bb.ip_networks(ip_list).cluster("IP address in network(s)",4).load_grades('Fall2015_Grades.csv','assignment3')

Performs one-way ANOVA test of midterm exam score across cluster groups set up on basis of on-campus vs. off-campus IP use:

ip_list = ["192.168.0.0/16","10.0.0.0/16"]
print bb.ip_networks(ip_list).cluster("IP address in network(s)").load_grades('Fall2015_Grades.csv','midterm').replace('-', 0).anova("midtern","cluster")

Performs one-way ANOVA test of midterm exam score across cluster groups set up on basis of mean weekday of activity:

print bb.mean_weekday_activity().cluster("Mean weekday").load_grades('Fall2015_Grades.csv','midterm').replace('-', 0).anova("midterm","cluster")

Runs an OLS regression model using midterm grades as dependent variable and (1) on-campus vs. off-campus IP use and (2) average weekday of content access as independent variables:

ip_list = ["192.168.0.0/16","10.0.0.0/16"]
ips = bb.ip_networks(ip_list)
ols = bb.mean_weekday_activity().load_grades('Fall2015_Grades.csv','midterm').replace('-', 0).merge(ips).ols("midterm",["Mean weekday","IP address in network(s)"])
print ols.summary()