def read_user_registrations(self):
"""
Read history of user registrations.
Returns:
Pandas DataFrame indexed by date with a single column
representing the number of users who have accounts at
the end of that day.
"""
with self.input()['registrations'].open('r') as registrations_file:
# The column name here will be converted in to a row name later when
# the data is transposed.
registration_changes = read_tsv(
registrations_file, ['date', self.ROW_LABELS['registrations']])
registration_changes.date = pandas.to_datetime(
registration_changes.date)
registration_changes.set_index(['date'], inplace=True)
cumulative_registrations = registration_changes.cumsum()
# Restrict the index to only the date range requested
date_range = pandas.date_range(self.start_date, self.date)
# Forward fill gaps because those dates have no change in registrations
cumulative_registrations = cumulative_registrations.reindex(
date_range, method='ffill')
return cumulative_registrations
def read_course_date_count_tsv(self, input_file):
"""Read TSV file with hard-coded column names into a pandas DataFrame."""
names = ['course_id', 'date', 'count']
# Not assuming any encoding, course_id will be read as plain string
data = read_tsv(input_file, names)
data.date = pandas.to_datetime(data.date)
return data
def read_course_date_count_tsv(self, input_file):
"""Read TSV file with hard-coded column names into a pandas DataFrame."""
names = ['course_id', 'date', 'count']
# Not assuming any encoding, course_id will be read as plain string
data = read_tsv(input_file, names)
data.date = pandas.to_datetime(data.date)
return data
def read_course_blacklist(self):
"""
Reads a set of course_ids from the blacklist input file if one was
specified, otherwise returns an empty set.
Expected input file format is a single course ID per line.
Returns:
A set of course_ids that should not be included in aggregates.
"""
if self.input().get('blacklist'):
with self.input()['blacklist'].open('r') as blacklist_file:
data = read_tsv(blacklist_file, ['course_id'])
return set(data['course_id'])
else:
return set()
def read_course_blacklist(self):
"""
Reads a set of course_ids from the blacklist input file if one was
specified, otherwise returns an empty set.
Expected input file format is a single course ID per line.
Returns:
A set of course_ids that should not be included in aggregates.
"""
if self.input().get('blacklist'):
with self.input()['blacklist'].open('r') as blacklist_file:
data = read_tsv(blacklist_file, ['course_id'])
return set(data['course_id'])
else:
return set()
def read_date_count_tsv(self, input_file):
"""
Read TSV containing dates and corresponding counts into a pandas Series.
NANs are not filled in here, as more than one filling strategy is
used with such files.
"""
names = ['date', 'count']
data = read_tsv(input_file, names)
data.date = pandas.to_datetime(data.date)
data = data.set_index('date')
# Ensure a continuos date range
date_range = pandas.date_range(min(data.index), max(data.index))
data = data.reindex(date_range)
# Return as a Series
return data['count']
def read_date_count_tsv(self, input_file):
"""
Read TSV containing dates and corresponding counts into a pandas Series.
NANs are not filled in here, as more than one filling strategy is
used with such files.
"""
names = ['date', 'count']
data = read_tsv(input_file, names)
data.date = pandas.to_datetime(data.date)
data = data.set_index('date')
# Ensure a continuos date range
date_range = pandas.date_range(min(data.index), max(data.index))
data = data.reindex(date_range)
# Return as a Series
return data['count']
def read_statuses(self):
"""
Read course statuses into a pandas DataFrame.
Returns:
Pandas dataframe with one row per course_id and
a column for the status. The status should
be either "past", "current" or "new". The index
for the DataFrame is the course_id.
Returns None if no statuses was specified.
"""
data = None
names = ['course_id', 'status']
if self.input().get('statuses'):
with self.input()['statuses'].open('r') as status_file:
data = read_tsv(status_file, names)
data = data.set_index('course_id')
return data
def read_statuses(self):
"""
Read course statuses into a pandas DataFrame.
Returns:
Pandas dataframe with one row per course_id and
a column for the status. The status should
be either "past", "current" or "new". The index
for the DataFrame is the course_id.
Returns None if no statuses was specified.
"""
data = None
names = ['course_id', 'status']
if self.input().get('statuses'):
with self.input()['statuses'].open('r') as status_file:
data = read_tsv(status_file, names)
data = data.set_index('course_id')
return data
def read_user_registrations(self):
"""
Read history of user registrations.
Returns:
Pandas DataFrame indexed by date with a single column
representing the number of users who have accounts at
the end of that day.
"""
with self.input()['registrations'].open('r') as registrations_file:
# The column name here will be converted in to a row name later when
# the data is transposed.
registration_changes = read_tsv(registrations_file, ['date', self.ROW_LABELS['registrations']])
registration_changes.date = pandas.to_datetime(registration_changes.date)
registration_changes.set_index(['date'], inplace=True)
cumulative_registrations = registration_changes.cumsum()
# Restrict the index to only the date range requested
date_range = pandas.date_range(self.start_date, self.date)
# Forward fill gaps because those dates have no change in registrations
cumulative_registrations = cumulative_registrations.reindex(date_range, method='ffill')
return cumulative_registrations
