In [1]:
import unicodecsv
## Longer version of code (replaced with shorter, equivalent version below)
# enrollments = []
# f = open('enrollments.csv', 'rb')
# reader = unicodecsv.DictReader(f)
# for row in reader:
# enrollments.append(row)
# f.close()
with open('enrollments.csv', 'rb') as f:
reader = unicodecsv.DictReader(f)
enrollments = list(reader)
enrollments[0]
Out[1]:
In [2]:
#####################################
# 1 #
#####################################
## Read in the data from daily_engagement.csv and project_submissions.csv
## and store the results in the below variables.
## Then look at the first row of each table.
def read_csv(filename):
with open(filename, 'rb') as f:
reader = unicodecsv.DictReader(f)
return list(reader)
daily_engagement = read_csv('daily_engagement.csv')
project_submissions = read_csv('project_submissions.csv')
In [3]:
from datetime import datetime as dt
# Takes a date as a string, and returns a Python datetime object.
# If there is no date given, returns None
def parse_date(date):
if date == '':
return None
else:
return dt.strptime(date, '%Y-%m-%d')
# Takes a string which is either an empty string or represents an integer,
# and returns an int or None.
def parse_maybe_int(i):
if i == '':
return None
else:
return int(i)
# Clean up the data types in the enrollments table
for enrollment in enrollments:
enrollment['cancel_date'] = parse_date(enrollment['cancel_date'])
enrollment['days_to_cancel'] = parse_maybe_int(enrollment['days_to_cancel'])
enrollment['is_canceled'] = enrollment['is_canceled'] == 'True'
enrollment['is_udacity'] = enrollment['is_udacity'] == 'True'
enrollment['join_date'] = parse_date(enrollment['join_date'])
enrollments[0]
Out[3]:
In [12]:
# Clean up the data types in the engagement table
for engagement_record in daily_engagement:
engagement_record['lessons_completed'] = int(float(engagement_record['lessons_completed']))
engagement_record['num_courses_visited'] = int(float(engagement_record['num_courses_visited']))
engagement_record['projects_completed'] = int(float(engagement_record['projects_completed']))
engagement_record['total_minutes_visited'] = float(engagement_record['total_minutes_visited'])
engagement_record['utc_date'] = parse_date(engagement_record['utc_date'])
daily_engagement[0]
Out[12]:
In [13]:
# Clean up the data types in the submissions table
for submission in project_submissions:
submission['completion_date'] = parse_date(submission['completion_date'])
submission['creation_date'] = parse_date(submission['creation_date'])
project_submissions[0]
Out[13]:
In [ ]:
#####################################
# 2 #
#####################################
## Find the total number of rows and the number of unique students (account keys)
## in each table.
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#####################################
# 3 #
#####################################
## Rename the "acct" column in the daily_engagement table to "account_key".
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#####################################
# 4 #
#####################################
## Find any one student enrollments where the student is missing from the daily engagement table.
## Output that enrollment.
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#####################################
# 5 #
#####################################
## Find the number of surprising data points (enrollments missing from
## the engagement table) that remain, if any.
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# Create a set of the account keys for all Udacity test accounts
udacity_test_accounts = set()
for enrollment in enrollments:
if enrollment['is_udacity']:
udacity_test_accounts.add(enrollment['account_key'])
len(udacity_test_accounts)
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# Given some data with an account_key field, removes any records corresponding to Udacity test accounts
def remove_udacity_accounts(data):
non_udacity_data = []
for data_point in data:
if data_point['account_key'] not in udacity_test_accounts:
non_udacity_data.append(data_point)
return non_udacity_data
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# Remove Udacity test accounts from all three tables
non_udacity_enrollments = remove_udacity_accounts(enrollments)
non_udacity_engagement = remove_udacity_accounts(daily_engagement)
non_udacity_submissions = remove_udacity_accounts(project_submissions)
print len(non_udacity_enrollments)
print len(non_udacity_engagement)
print len(non_udacity_submissions)
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#####################################
# 6 #
#####################################
## Create a dictionary named paid_students containing all students who either
## haven't canceled yet or who remained enrolled for more than 7 days. The keys
## should be account keys, and the values should be the date the student enrolled.
paid_students =
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# Takes a student's join date and the date of a specific engagement record,
# and returns True if that engagement record happened within one week
# of the student joining.
def within_one_week(join_date, engagement_date):
time_delta = engagement_date - join_date
return time_delta.days < 7
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#####################################
# 7 #
#####################################
## Create a list of rows from the engagement table including only rows where
## the student is one of the paid students you just found, and the date is within
## one week of the student's join date.
paid_engagement_in_first_week =
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from collections import defaultdict
# Create a dictionary of engagement grouped by student.
# The keys are account keys, and the values are lists of engagement records.
engagement_by_account = defaultdict(list)
for engagement_record in paid_engagement_in_first_week:
account_key = engagement_record['account_key']
engagement_by_account[account_key].append(engagement_record)
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# Create a dictionary with the total minutes each student spent in the classroom during the first week.
# The keys are account keys, and the values are numbers (total minutes)
total_minutes_by_account = {}
for account_key, engagement_for_student in engagement_by_account.items():
total_minutes = 0
for engagement_record in engagement_for_student:
total_minutes += engagement_record['total_minutes_visited']
total_minutes_by_account[account_key] = total_minutes
In [ ]:
import numpy as np
# Summarize the data about minutes spent in the classroom
total_minutes = total_minutes_by_account.values()
print 'Mean:', np.mean(total_minutes)
print 'Standard deviation:', np.std(total_minutes)
print 'Minimum:', np.min(total_minutes)
print 'Maximum:', np.max(total_minutes)
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#####################################
# 8 #
#####################################
## Go through a similar process as before to see if there is a problem.
## Locate at least one surprising piece of data, output it, and take a look at it.
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#####################################
# 9 #
#####################################
## Adapt the code above to find the mean, standard deviation, minimum, and maximum for
## the number of lessons completed by each student during the first week. Try creating
## one or more functions to re-use the code above.
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######################################
# 10 #
######################################
## Find the mean, standard deviation, minimum, and maximum for the number of
## days each student visits the classroom during the first week.
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######################################
# 11 #
######################################
## Create two lists of engagement data for paid students in the first week.
## The first list should contain data for students who eventually pass the
## subway project, and the second list should contain data for students
## who do not.
subway_project_lesson_keys = ['746169184', '3176718735']
passing_engagement =
non_passing_engagement =
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######################################
# 12 #
######################################
## Compute some metrics you're interested in and see how they differ for
## students who pass the subway project vs. students who don't. A good
## starting point would be the metrics we looked at earlier (minutes spent
## in the classroom, lessons completed, and days visited).
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######################################
# 13 #
######################################
## Make histograms of the three metrics we looked at earlier for both
## students who passed the subway project and students who didn't. You
## might also want to make histograms of any other metrics you examined.
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######################################
# 14 #
######################################
## Make a more polished version of at least one of your visualizations
## from earlier. Try importing the seaborn library to make the visualization
## look better, adding axis labels and a title, and changing one or more
## arguments to the hist() function.