cursor = collection['format_tests'].find({'parent_data.chapter_display_name' : 'Test 1'})
users_sessions = defaultdict(list)
for index,item in enumerate(cursor):
#print index, item['parent_data']['chapter_display_name']
users_sessions[(item['username'], item['session'])].append(item['time'])
users_tests_events = defaultdict(int)
for (username,session),times in users_sessions.iteritems():
end_time = datetime.strptime(max(times).split('+')[0], "%Y-%m-%dT%H:%M:%S.%f")
start_time = datetime.strptime(min(times).split('+')[0], "%Y-%m-%dT%H:%M:%S.%f")
cursor = collection['tracking'].find({'username' : username, 'session' : session, '$or' : [{'event_type' : 'seq_goto'},{'event_type':'seq_prev'},{'event_type' : 'seq_next'}]})
for index, document in enumerate(cursor):
try:
time_stamp = datetime.strptime(document['time'].split('+')[0], "%Y-%m-%dT%H:%M:%S.%f")
if start_time <= time_stamp <= end_time:
if 'sequential_display_name' in document['parent_data'] and document['parent_data']['sequential_display_name']:
sequential_display_name = document['parent_data']['sequential_display_name']
elif document['metadata']['display_name']:
sequential_display_name = document['metadata']['display_name']
else:
sequential_display_name = None
#users_tests_events[(username, session, document['parent_data'].get('chapter_display_name', None),document['parent_data'].get('sequential_display_name', None))] += 1
users_tests_events[(username, session, document['parent_data'].get('chapter_display_name', None),sequential_display_name)] += 1
except:
print index,document
result = []
for (username,session,chapter_name, sequential_name) in users_tests_events:
result.append([username,session,chapter_name, sequential_name, users_tests_events[(username,session,chapter_name, sequential_name)]])
output = CSV(result, ['Username', 'Session ID', 'Chapter Display Name', 'Sequential Display Name', 'Navigation Count'], output_file='test1_analysis.csv')
output.generate_csv()
'''
This module determines how many chapters were accessed by each user for a
given course
Usage:
python chapters_accessed_per_user
'''
from collections import defaultdict
from base_edx import EdXConnection
from generate_csv_report import CSV
connection = EdXConnection('tracking', 'course_structure')
collection = connection.get_access_to_collection()
# Get all chapters
chapters = collection['course_structure'].distinct(
'parent_data.chapter_display_name')
tracking = collection['tracking'].find()
result = []
for document in tracking:
if 'parent_data' in document:
pass
output = CSV(result, ['Username'].extend(chapters),
output_file='atoc185x_chapters_accesses_per_user.csv')
output.generate_csv()
'$match': {
'username': {
'$in': usernames
},
'$or': [{
'event_type': 'play_video'
}, {
'event_type': 'problem_check',
'event_source': 'server'
}]
}
}, {
'$group': {
'_id': {
"username": "******",
"chapter_name": "$parent_data.chapter_display_name",
"sequential_name": "$parent_data.sequential_display_name",
"vertical_name": "$parent_data.vertical_display_name"
}
}
}]) #, {'$out' : 'students_50_to_59_events'}])
result = [[
document['_id']['username'], document['_id']['chapter_name'],
document['_id']['sequential_name'], document['_id']['vertical_name']
] for document in cursor['result'] if 'chapter_name' in document['_id']]
output = CSV(result,
['Username', 'Chapter Name', 'Sequential Name', 'Vertical Name'],
output_file='failure_analysis_50_to_59.csv')
output.generate_csv()
item['_id'], item['parent_data']['chapter_display_name'],
item['metadata']['display_name'],
len(children)
]
aggregate_vertical = defaultdict(int)
aggregate_category = defaultdict(int)
for _id in children:
try:
vertical = collection['course_structure'].find_one({'_id': _id})
aggregate_vertical[vertical['category']] += 1
for _id in vertical['children']:
child = collection['course_structure'].find_one({'_id': _id})
aggregate_category[child['category']] += 1
except Exception:
pass
temp_result.extend([
aggregate_category['video'], aggregate_category['html'],
aggregate_category['problem'], aggregate_category['discussion'],
aggregate_category['poll_question'], aggregate_category['word_cloud']
])
result.append(temp_result)
output = CSV(result, [
'Sequential ID', 'Chapter Display Name', 'Sequential Name',
'Number of Verticals', 'Number of Videos', 'Number of HTML',
'Number of Problems', 'Number of Discussions', 'Number of Poll Questions',
'Number of Word Cloud'
],
output_file='sequential_aggregation.csv')
output.generate_csv()
try:
country_code = geoip.country(value)
country = country_code_to_country[country_code]
if not key:
key = 'anonymous'
ip_to_country.append([key, value, country_code, country])
elif (key, country) not in country_set:
country_set.add((key, country))
ip_to_country.append([key, value, country_code, country])
except:
# IMPORTANT
# The following code for an exception are hardcoded for those IPs which do have a mapping to a
# country code but they were not available in GeoIP.dat (most probably because it was not updated)
# People using this script can either report this code (under except) and or additional conditions
# IP addresses which cannot be mapped to a country code stored in GeoIP.dat
if value == '41.79.120.29':
country = country_code_to_country['SS']
if not key:
key = 'anonymous'
ip_to_country.append(
[key, value, 'SS', country_code_to_country['SS']])
elif (key, country) not in country_set:
country_set.add((key, country))
ip_to_country.append(
[key, value, 'SS', country_code_to_country['SS']])
output = CSV(ip_to_country,
['Username', 'IP Address', 'Country Code', 'Country'],
output_file=db_name + '_ip_to_country.csv')
output.generate_csv()
else:
student_answers_values[key] = values
else:
if "choice_" in values:
mapped_values = answer_keys[key].values()
student_answers_values[key] = mapped_values
else:
student_answers_values[key] = values
if username not in output:
output[username] = {'problem_id' : problem_id, 'student_answers' : student_answers_values, 'attempts' : student_attempts}
else:
if student_attempts > output[username]['attempts']:
output[username]['problem_id'] = problem_id
output[username]['student_answers'] = student_answers_values
output[username]['attempts'] = student_attempts
except:
print "Key Error!!!", key, values, problem_id
result = []
for key, values in output.iteritems():
username = key
problem_id = values['problem_id']
for question, answer in values['student_answers'].iteritems():
result.append([username, problem_id, question, answer])
output = CSV(result, ['Username','Problem ID', 'Student Answers' ], output_file=db_name + '_student_answers.csv')
output.generate_csv()
#with open(db_name + '_student_answers', 'w') as f:
# json.dump(output, f)
'''
import csv
from base_edx import EdXConnection
from generate_csv_report import CSV
connection = EdXConnection('tracking_atoc185x')
collection = connection.get_access_to_collection()
# Can replace csv file with any csv file that contains the list of usernames
# who completed the course and achieved a certificate. Alternately, one can
# save that info in another collection in mongoDB and extra it from the collection
with open('atoc185x/course_completers.csv', 'r') as csv_file:
reader = csv.reader(csv_file)
reader.next()
usernames = {row[1] for row in reader}
cursor = collection['tracking_atoc185x'].find(
{'event_type': 'show_transcript'})
result = []
seen = set()
for document in cursor:
if document['username'] in usernames and document['username'] not in seen:
seen.add(document['username'])
result.append([document['username']])
output = CSV(result, ['Username'],
output_file='show_transcript_completers.csv')
output.generate_csv()
def _generate_name_from_problem_id(problem_id):
'''
Generate name of csv output file from problem id
'''
return '_'.join(problem_id.split('/')[3:]) + '.csv'
cursor = collection['atoc185x_problem_ids'].find(
{'event.problem_id': sys.argv[1]})
#cursor = collection['atoc185x_problem_ids'].aggregate([{'$match' :
#{'problem_id':sys.argv[1]}}, {'$group' : { '_id' : {'username' : '$username',
#'attempt_number' : '$event.attempts', 'time' : '$time','answers' : '$event.answers',
#'success' : '$event.success', 'grade' : '$event.grade', 'max_grade' : '$event.max_grade'}}}])
result = []
for document in cursor:
result.append([
document['username'], document['event']['attempts'],
document['module']['display_name'], document['time'],
document['event']['success'], document['event']['grade'],
document['event']['max_grade'], document['event']['answers']
])
csv_report_name = _generate_name_from_problem_id(sys.argv[1])
output = CSV(result, [
'Username', 'Attempt Number', 'Module', 'Time', 'Success',
'Grade Achieved', 'Max Grade', 'Answers'
],
output_file=csv_report_name)
output.generate_csv()
'''
This module extracts the student IDs from the collection certificates_generatedcertificate
of the students who completed the course and achieved a certificate. The ids
are then used to extract the usernames of the course completers
Usage:
python course_completers.py
'''
from collections import defaultdict
from base_edx import EdXConnection
from generate_csv_report import CSV
connection = EdXConnection('certificates_generatedcertificate', 'auth_user')
collection = connection.get_access_to_collection()
completers = collection['certificates_generatedcertificate'].find({'status' : 'downloadable'})
result = []
for document in completers:
user_document = collection['auth_user'].find_one({"id" : document['user_id']})
result.append([user_document['id'],user_document['username'], document['name'], document['grade']])
output = CSV(result, ['User ID','Username', 'Name', 'Grade'], output_file='course_completers.csv')
output.generate_csv()
from base_edx import EdXConnection
from generate_csv_report import CSV
connection = EdXConnection('seek_video')
collection = connection.get_access_to_collection()
sort_parameters = [('parent_data.chapter_display_name', 1),
('parent_data.sequential_display_name', 1),
('parent_data.vertical_display_name', 1)]
cursor = collection['seek_video'].find()
result = []
for index, item in enumerate(cursor):
if 'old_time' in item['event']:
old_time = item['event']['old_time']
else:
old_time = 0
result.append([
item['username'], item['parent_data']['chapter_display_name'],
item['parent_data']['sequential_display_name'],
item['parent_data']['vertical_display_name'], old_time,
item['event']['new_time']
])
output = CSV(result, [
'Username', 'Chapter Name', 'Sequential Name', 'Vertical Name', 'Old Time',
'New Time'
],
output_file='seek_video.csv',
row_limit=200000)
output.generate_csv()
username, video associated with load_video event, parent_data: {chapter_display_name, sequential_display_name, vertical_display_name,}, edx_video_id, video watch segments
get the event_types : load_video, play_video, pause_video, seek_video
sort by "time": "" so that the events are chronologically ordered
for each load_video new video watch segment should include ONLY:
- time between play_video -> next video event in time (pause_video or seek_video)
- time between seek_video : {'new_time' : Time} -> pause video (only with new_time > old_time, this is to avoid including rewinds)
watch periods:
event_type : pause_video - "event_type":"play_video" {"event":{"currentTime":TIME}} = new video watch segment
if seek_video : {'old_time' : TIME} < seek_video : {'new_time' : TIME}
"pause_video" {"event":{"currentTime":TIME}} - seek_video : {'new_time': TIME } = new video watch segment
if seek_video : {'old_time' : TIME} > seek_video : {'new_time' : TIME} = rewind (exclude from watch segments)
'''
from base_edx import EdXConnection
from generate_csv_report import CSV
connection = EdXConnection('video_watch_duration_collection')
collection = connection.get_access_to_collection()
cursor = collection['video_watch_duration_collection'].find()
result = []
output = CSV(result,['Username',], output_file='video_watch_duration.csv', row_limit=200000)
output.generate_csv()
from collections import defaultdict
import json
import sys
from base_edx import EdXConnection
from generate_csv_report import CSV
db_name = sys.argv[1]
# Change name of collection as required
connection = EdXConnection(db_name, 'forum')
collection = connection.get_access_to_collection()
forum_data = collection['forum'].find()
csv_data = []
for document in forum_data:
csv_data.append([
document['_id']['oid'], document['author_username'], document['_type'],
document.get('title', ''), document['body'],
document['created_at']['date']
])
headers = ['ID', 'Author Username', 'Type', 'Title', 'Body', 'Created At Date']
output = CSV(csv_data, headers, output_file=db_name + '_forum_data.csv')
output.generate_csv()
print "Fail -> %s" % item
fail.append(item)
print "Number of fail: " + str(len(fail))
if fail:
import json
with open('report.txt', 'w') as outfile:
json.dump(fail, outfile)
else:
print "no fail"
result = []
for item in users_to_sessions:
for nested_item in users_to_sessions[item]:
max_time = max(users_to_sessions[item][nested_item])
end_time = datetime.strptime(
max_time.split('+')[0], "%Y-%m-%dT%H:%M:%S.%f")
min_time = min(users_to_sessions[item][nested_item])
start_time = datetime.strptime(
min_time.split('+')[0], "%Y-%m-%dT%H:%M:%S.%f")
result.append([
item, nested_item,
len(users_to_sessions[item][nested_item]), start_time, end_time,
end_time - start_time
])
output = CSV(result, [
'Username', 'Session ID', 'Number of Events', 'Start Time', 'End Time',
'Time Spent'
],
output_file='session_info.csv')
output.generate_csv()
Usage:
python activities_with_lower_completion.py
'''
from collections import defaultdict
from base_edx import EdXConnection
from generate_csv_report import CSV
# Connect to MongoDB and extra the tracking collection
connection = EdXConnection('user_attempts_per_problem_id')
collection = connection.get_access_to_collection()
cursor = collection['user_attempts_per_problem_id'].find()
result = defaultdict(lambda: defaultdict(int))
for index, document in enumerate(cursor):
# If there is a correct attempts, accept as answered correctly, else accept
#as incorrect only once per student per problem id
if 'correct' in document['attempts']:
result[document['_id']['problem_id']]['correct'] += 1
else:
result[document['_id']['problem_id']]['incorrect'] += 1
csv_result = [[item, result[item]['correct'], result[item]['incorrect']]
for item in result]
output = CSV(csv_result, ['Problem Id', 'Correct Count', 'Incorrect Count'],
output_file='activities_with_lower_completion.csv')
output.generate_csv()
with open('csv_files/McGillX_CHEM181x_1T2014_grade_report_2014-04-24-1030.csv', 'r') as csv_file:
reader = csv.reader(csv_file)
reader.next()
usernames = [row[2] for row in reader]
NAVIGATION_TABS = {'/courses/McGillX/ATOC185x/2T2014/info' : 'info', '/courses/McGillX/ATOC185x/2T2014/progress' : 'progress', '/courses/McGillX/ATOC185x/2T2014/109d5374b52040e2a8b737cf90c5618a/' : 'syllabus', '/courses/McGillX/ATOC185x/2T2014/441b2c519f5c464883e2ddceb26c5559/' : 'maps','/courses/McGillX/ATOC185x/2T2014/84f630e833eb4dbabe0a6c45c52bb443/' : 'scoreboard' , '/courses/McGillX/ATOC185x/2T2014/e75195cb39fa4e3890a613a1b3c04c7d/' : 'faq', 'courseware' : 'courseware', 'discussion': 'discussion', '/courses/McGillX/ATOC185x/2T2014/instructor' : 'instructor'}
cursor = collection['tracking_atoc185x'].find({'username' : {'$in' : usernames},'event_type' : { '$regex' : '^/courses/McGillX/ATOC185x/2T2014/(info$|progress$|instructor$|109d5374b52040e2a8b737cf90c5618a/$|441b2c519f5c464883e2ddceb26c5559/$|84f630e833eb4dbabe0a6c45c52bb443/$|e75195cb39fa4e3890a613a1b3c04c7d/$|courseware|discussion)'}})
tab_events_per_date = defaultdict(int)
for doc in cursor:
date = datetime.strptime(doc['time'].split('T')[0], "%Y-%m-%d").date()
if 'courseware' in doc['event_type']:
tab_events_per_date[(date,'courseware')] += 1
elif 'discussion' in doc['event_type']:
tab_events_per_date[(date, 'discussion')] += 1
else:
tab_events_per_date[(date, doc['event_type'])] += 1
result = []
for date, tab in tab_events_per_date:
result.append([date,tab, tab_events_per_date[(date,tab)]])
output = CSV(result, ['Date','Tab ID','Number of Events'], output_file='number_of_tab_events_per_date_completers.csv')
output.generate_csv()
#with open('csv_files/number_of_tab_events_per_date_completers.csv', 'w') as csv_file:
# writer = csv.writer(csv_file)
# writer.writerow(['Date','Tab ID','Number of Events'])
# for date,tab in tab_events_per_date:
# writer.writerow([date,tab, tab_events_per_date[(date,tab)] ])
from generate_csv_report import CSV
connection = EdXConnection('tracking_atoc185x')
collection = connection.get_access_to_collection()
with open('csv_files/McGillX_CHEM181x_1T2014_grade_report_2014-04-24-1030.csv', 'r') as csv_file:
reader = csv.reader(csv_file)
usernames = [row[2] for row in reader]
cursor = collection['tracking_atoc185x'].aggregate([{"$match" : {"event_source" : "browser", "$or" : [{"event_type" : "seq_prev"},{"event_type" : "seq_goto"},{"event_type" : "seq_next"}], 'username' : {'$in' : usernames}}}, {"$group" : {"_id" : {'chapter_name' : "$parent_data.chapter_display_name", "display_name" : "$metadata.display_name", "event_type" : "$event_type", "event_old" : "$event.old", "event_new" : "$event.new"}, "count" : {"$sum" : 1}}}])
#with open('csv_files/navigation_frequency_completers.csv', 'w') as csv_file:
# writer = csv.writer(csv_file)
# writer.writerow(['Chapter Name', 'Display Name', 'Event Type', 'Event Old', 'Event New', 'Count'])
# for item in cursor['result']:
# try:
# writer.writerow([item['_id']['chapter_name'], item['_id']['display_name'], item['_id']['event_type'], item['_id'].get('event_old', 0), item['_id']['event_new'], item['count']])
# except:
# pass
result = []
for item in cursor['result']:
try:
result.append([item['_id']['chapter_name'], item['_id']['display_name'], item['_id']['event_type'], item['_id'].get('event_old', 0), item['_id']['event_new'], ite m['count']])
except:
pass
output = CSV(result, ['Chapter Name', 'Display Name', 'Event Type', 'Event Old', 'Event New', 'Count'], output_file='navigation_frequency_completers.csv')
output.generate_csv()
from datetime import datetime
from base_edx import EdXConnection
from generate_csv_report import CSV
connection = EdXConnection('student_courseenrollment')
collection = connection.get_access_to_collection()
# Can replace csv file with any csv file that contains the list of usernames
# who completed the course and achieved a certificate. Alternately, one can
# save that info in another collection in mongoDB and extra it from the collection
with open('atoc185x/course_completers.csv') as csv_file:
reader = csv.reader(csv_file)
reader.next()
users = {row[0]: row[1] for row in reader}
result = []
student_courseenrollment = collection['student_courseenrollment'].find()
seen = set()
for document in student_courseenrollment:
if str(document['user_id']) in users and document['user_id'] not in seen:
seen.add(document['user_id'])
result.append([
document['user_id'], users[str(document['user_id'])],
document['created'].split()[0]
])
output = CSV(result, ['Username', 'Date of Registration'],
output_file='date_of_registration_completers.csv')
output.generate_csv()
'''
import sys
import csv
from base_edx import EdXConnection
from generate_csv_report import CSV
db_name = sys.argv[1]
# Change name of collection as required
connection = EdXConnection(db_name, 'user_id_map' )
collection = connection.get_access_to_collection()
with open(sys.argv[2]) as f:
headers = next(f)
reader = csv.reader(f)
data = [row for row in reader]
result = []
for row in data:
cursor = collection['user_id_map'].find_one({'id' : long(row[0])})
hash_id = cursor['hash_id']
username = cursor['username']
result.append([row[0], username, hash_id] + row[1:])
input_file, extension = sys.argv[2].split('.')
output = CSV(result, [headers.split(',')[0],'Username','User Hash ID'] + headers.split(',')[1:], output_file=input_file+'_userid_anon.'+extension)
output.generate_csv()
'^/courses/McGillX/ATOC185x/2T2014/(info$|progress$|instructor$|109d5374b52040e2a8b737cf90c5618a/$|441b2c519f5c464883e2ddceb26c5559/$|84f630e833eb4dbabe0a6c45c52bb443/$|e75195cb39fa4e3890a613a1b3c04c7d/$|courseware|discussion)'
}
})
unique_users_per_tab = defaultdict(set)
for doc in cursor:
if 'courseware' in doc['event_type']:
unique_users_per_tab['courseware'].add(doc['username'])
elif 'discussion' in doc['event_type']:
unique_users_per_tab['discussion'].add(doc['username'])
else:
unique_users_per_tab[doc['event_type']].add(doc['username'])
#with open('csv_files/number_of_unique_users_per_navigation_tab.csv', 'w') as csv_file:
# writer = csv.writer(csv_file)
# writer.writerow(['Navigation Tab', 'Number of Unique Users'])
# for key in unique_users_per_tab:
# writer.writerow([key, len(unique_users_per_tab[key])])
#with open('csv_files/users_per_navigation_tab.csv', 'w') as csv_file:
# writer = csv.writer(csv_file)
# writer.writerow(['Navigation Tab','Tab', 'Number of Unique Users'])
# for key in unique_users_per_tab:
# writer.writerow([key,NAVIGATION_TABS[key] ,len(unique_users_per_tab[key])])
result = []
for key in unique_users_per_tab:
result.append([key, NAVIGATION_TABS[key], len(unique_users_per_tab[key])])
output = CSV(result, ['Navigation Tab', 'Tab', 'Number of Unique Users'],
output_file='number_of_unique_users_per_navigation_tab.csv')
output.generate_csv()
not_in_auth_user.add(document['student_id'])
# For loop to retrieve the names of all the survey pages. Since a student may
# not have filled all pages, we look for the longest list and use the values
# to retrieve the survey pages
survey_question_ids = {}
for value in result.values():
if len(value) == 5:
temp = {key for item in value for key in item.keys()}
if len(temp) > len(survey_question_ids):
survey_question_ids = temp
survey_question_ids = sorted(list(survey_question_ids))
csv_data = []
for username, survey_info in result.iteritems():
temp = [''] * len(survey_question_ids)
for item in survey_info:
for key, value in item.iteritems():
try:
index = survey_question_ids.index(key)
if key in survey_question_ids:
temp[index] = value
except:
pass
temp.insert(0, username)
csv_data.append(temp)
output = CSV(csv_data, ['Username'] + survey_question_ids,
output_file=db_name + '_entrance_exit_surveys.csv')
output.generate_csv()
student_answers_values[key] = mapped_values
else:
student_answers_values[key] = values
if username not in output:
output[username] = {
'problem_id': problem_id,
'student_answers': student_answers_values,
'attempts': student_attempts
}
else:
if student_attempts > output[username]['attempts']:
output[username]['problem_id'] = problem_id
output[username]['student_answers'] = student_answers_values
output[username]['attempts'] = student_attempts
except:
print "Key Error!!!", key, values, problem_id
result = []
for key, values in output.iteritems():
username = key
problem_id = values['problem_id']
for question, answer in values['student_answers'].iteritems():
result.append([username, problem_id, question, answer])
output = CSV(result, ['Username', 'Problem ID', 'Student Answers'],
output_file=db_name + '_student_answers.csv')
output.generate_csv()
#with open(db_name + '_student_answers', 'w') as f:
# json.dump(output, f)
from base_edx import EdXConnection
from generate_csv_report import CSV
connection = EdXConnection('certificates_generatedcertificate',
'auth_userprofile')
collection = connection.get_access_to_collection()
documents = collection['auth_userprofile'].find()
result = []
for document in documents:
user_id = document['user_id']
try:
final_grade = collection['certificates_generatedcertificate'].find_one(
{'user_id': user_id})['grade']
result.append([
user_id, document['name'], final_grade, document['gender'],
document['year_of_birth'], document['level_of_education'],
document['country'], document['city']
])
except:
# Handle users with no grades
pass
output = CSV(result, [
'User ID', 'Username', 'Final Grade', 'Gender', 'Year of Birth',
'Level of Education', 'Country', 'City'
],
output_file='atoc185x_user_info.csv')
output.generate_csv()
Since we will need to sort a very large number of documents, you should create a separate collection to
aggregate all required documents in one collection and then extract results from the new collection.
Command to run on the mongo shell to create new collection:
db.tracking_atoc185x.aggregate([{$match : {$and : [{"event_type" : "speed_change_video"},{ "parent_data": { $exists: true } }]}}, {$sort : {"parent_data.chapter_display_name" : 1, "parent_data.sequential_display_name" : 1, "parent_data.vertical_display_name" : 1}}, {$out : "speed_change_video_data"}], {allowDiskUse : true})
Usage:
python speed_change_video.py
'''
from base_edx import EdXConnection
from generate_csv_report import CSV
connection = EdXConnection('speed_change_video_data')
collection = connection.get_access_to_collection()
cursor = collection['speed_change_video_data'].find()
result = [[
item['username'], item['parent_data']['chapter_display_name'],
item['parent_data']['sequential_display_name'],
item['parent_data']['vertical_display_name'], item['event']['old_speed'],
item['event']['new_speed']
] for item in cursor]
output = CSV(result, [
'Username', 'Chapter Name', 'Sequential Name', 'Vertical Name',
'Old Speed', 'New Speed'
],
output_file='speed_change.csv')
output.generate_csv()