def ap(noble_aps):
#initialize new df
ap_tests = pd.DataFrame()
# add id
ap_tests['noble_powerschool_id'] = noble_aps['student_number']
# add date
ap_tests['date'] = pd.to_datetime(noble_aps['test_date'])
# add score
ap_tests['score'] = noble_aps['numscore']
# load ap dictionary from manually coded csv
subject_dict = create_conversion_dict(config.PERSISTENCE_PATH + '/code/etl/mappers/APsubjecttranslation.csv')
# do conversions from freetext to coded options
noble_aps.testname = noble_aps.testname.apply(lambda x: x.replace('AP ', '')) #remove the 'AP '
ap_tests['subject'] = noble_aps.testname.fillna('missing').apply(lambda x: convert_free_text(subject_dict, x))
return ap_tests
def ap(noble_aps):
#initialize new df
ap_tests = pd.DataFrame()
# add id
ap_tests['noble_powerschool_id'] = noble_aps['student_number']
# add date
ap_tests['date'] = pd.to_datetime(noble_aps['test_date'])
# add score
ap_tests['score'] = noble_aps['numscore']
# load ap dictionary from manually coded csv
subject_dict = create_conversion_dict(
config.PERSISTENCE_PATH + '/code/etl/mappers/APsubjecttranslation.csv')
# do conversions from freetext to coded options
noble_aps.testname = noble_aps.testname.apply(
lambda x: x.replace('AP ', '')) #remove the 'AP '
ap_tests['subject'] = noble_aps.testname.fillna('missing').apply(
lambda x: convert_free_text(subject_dict, x))
return ap_tests
def enrollments(enrollment):
# initialize clean dataframe
clean_enrollment = pd.DataFrame()
# add student and college ids
clean_enrollment['noble_student_sf_id'] = enrollment['Student__c']
clean_enrollment['noble_college_sf_id'] = enrollment['College__c']
# convert date columns to datetime to do date cleaning
enrollment['Start_Date__c'] = pd.to_datetime(enrollment['Start_Date__c'])
enrollment['End_Date__c'] = pd.to_datetime(enrollment['End_Date__c'])
enrollment['Date_Last_Verified__c'] = pd.to_datetime(enrollment['Date_Last_Verified__c'])
#remove weird dates, add to new df
clean_enrollment['start_date'] = enrollment.Start_Date__c.apply(wrong_dates_to_null)
clean_enrollment['end_date'] = enrollment.End_Date__c.apply(wrong_dates_to_null)
clean_enrollment['date_last_verified'] = enrollment.Date_Last_Verified__c.apply(wrong_dates_to_null)
# add status, data source, and degree type as is
clean_enrollment['status'] = enrollment.Status__c
clean_enrollment['data_source'] = enrollment.Data_Source__c
# Living on campus
clean_enrollment['living_on_campus'] = np.nan
# Degree type, without apostrophes
clean_enrollment['degree_type'] = enrollment.Degree_Type__c.dropna().apply(lambda x: str(x).replace("'",""))
# load degree and major dictionaries from manually coded csvs
degree_dict = create_conversion_dict(config.PERSISTENCE_PATH + '/code/etl/mappers/degreetranslation.csv')
major_dict = create_conversion_dict(config.PERSISTENCE_PATH + '/code/etl/mappers/majortranslation.csv')
# do conversions from freetext to coded options
clean_enrollment['degree_subject'] = enrollment.Degree_Text__c.fillna('missing').apply(lambda x: convert_free_text(degree_dict, x))
clean_enrollment['major'] = enrollment.Major_Text__c.fillna('missing').apply(lambda x: convert_free_text(major_dict, x))
# convert withdrawal reasons to several boolean categories
def boolean_withdrawal(dummy_code, input_code):
if input_code == 'missing':
return np.nan
else:
codes = input_code.split(';')
dummy_booleans = [True if code == dummy_code else False for code in codes]
if sum(dummy_booleans) > 0:
return True
else: return False
withdrawal_codes = ['Financial', 'Academic', 'Motivational', 'Family', 'Health', 'Social', 'Racial Conflict']
for dummy_code in withdrawal_codes:
clean_enrollment['withdrawal_reason_' + "_".join(dummy_code.lower().split())] = enrollment.Withdrawal_code__c.fillna('missing').apply(lambda x: boolean_withdrawal(dummy_code, x))
clean_enrollment.rename(columns={'withdrawal_reason_racial_conflict': 'withdrawal_reason_racial'}, inplace=True)
# Return clean df
return clean_enrollment
def enrollments_table(kipp_nj_enrollments):
'''Cleans KIPP NJ enrollment data to match our database schema'''
# remove the did not enroll, other, and deferred enrollment types
clean_enrollments = kipp_nj_enrollments[~kipp_nj_enrollments.status.isin(['Other', 'Did Not Enroll', 'Deferred'])]
# rename Withdrawn to withdrew, matriculated to matriculating
clean_enrollments.status = clean_enrollments.status.apply(lambda x: map_value_from_dict(status_fixed_mapping, x))
# made dates into dates
clean_enrollments.start_date = pd.to_datetime(clean_enrollments.start_date)
clean_enrollments.end_date = pd.to_datetime(clean_enrollments.end_date)
clean_enrollments.date_last_verified = pd.to_datetime(clean_enrollments.date_last_verified)
# deal with degree type
#remove high school diploma and GED, only interested in college enrollments
clean_enrollments = clean_enrollments[~clean_enrollments.degree_type.isin(['High School Diploma', 'GED'])]
clean_enrollments.degree_type = clean_enrollments.degree_type.apply(lambda x: map_value_from_dict(degree_fixed_mapping, x))
# clean up degree subject
clean_enrollments.degree_subject= clean_enrollments.degree_type.combine(clean_enrollments.degree_subject, func = code_degree_subject)
# clean up major
major_dict = create_conversion_dict(config.PERSISTENCE_PATH + '/code/etl/mappers/majortranslation.csv')
# do conversions from freetext to coded options
clean_enrollments['major'] = clean_enrollments.major.fillna('missing').apply(lambda x: convert_free_text(major_dict, x))
#map transfer reasons to the reasons we keep track of
withdrawal_reasons = pd.get_dummies(clean_enrollments.transfer_reason__c).astype(bool)
withdrawal_reasons.columns = ['withdrawal_reason_academic', 'withdrawal_reason_career', 'withdrawal_reason_financial', 'withdrawal_reason_other', 'withdrawal_reason_placement', 'withdrawal_reason_relocation', 'withdrawal_reason_social']
withdrawal_reasons.drop(['withdrawal_reason_relocation', 'withdrawal_reason_placement', 'withdrawal_reason_other', 'withdrawal_reason_career'], axis = 1,inplace = True)
# join the columns back into the original
clean_enrollments = clean_enrollments.join(withdrawal_reasons)
clean_enrollments['withdrawal_reason_motivational'] = np.nan
clean_enrollments['withdrawal_reason_family'] = np.nan
clean_enrollments['withdrawal_reason_health'] = np.nan
clean_enrollments['withdrawal_reason_racial'] = np.nan
# Drop invalid IPEDS id
clean_enrollments['college_ncesid'] = clean_enrollments['college_ncesid'].convert_objects(convert_numeric=True)
clean_enrollments.loc[clean_enrollments['college_ncesid'] > 999999, 'college_ncesid'] = np.nan
clean_enrollments['college_ncesid'] = clean_enrollments['college_ncesid'].apply(int_with_NaN_tostr)
clean_enrollments.drop(['transfer_reason__c', 'college_salesforce_id'],axis = 1,inplace = True)
clean_enrollments.rename(columns={'student_salesforce_id':'kipp_nj_sf_id', 'college_ncesid': 'ipedsid'}, inplace=True)
return clean_enrollments
def enrollments(enrollment):
# initialize clean dataframe
clean_enrollment = pd.DataFrame()
# add student and college ids
clean_enrollment['noble_student_sf_id'] = enrollment['Student__c']
clean_enrollment['noble_college_sf_id'] = enrollment['College__c']
# convert date columns to datetime to do date cleaning
enrollment['Start_Date__c'] = pd.to_datetime(enrollment['Start_Date__c'])
enrollment['End_Date__c'] = pd.to_datetime(enrollment['End_Date__c'])
enrollment['Date_Last_Verified__c'] = pd.to_datetime(
enrollment['Date_Last_Verified__c'])
#remove weird dates, add to new df
clean_enrollment['start_date'] = enrollment.Start_Date__c.apply(
wrong_dates_to_null)
clean_enrollment['end_date'] = enrollment.End_Date__c.apply(
wrong_dates_to_null)
clean_enrollment[
'date_last_verified'] = enrollment.Date_Last_Verified__c.apply(
wrong_dates_to_null)
# add status, data source, and degree type as is
clean_enrollment['status'] = enrollment.Status__c
clean_enrollment['data_source'] = enrollment.Data_Source__c
# Living on campus
clean_enrollment['living_on_campus'] = np.nan
# Degree type, without apostrophes
clean_enrollment['degree_type'] = enrollment.Degree_Type__c.dropna().apply(
lambda x: str(x).replace("'", ""))
# load degree and major dictionaries from manually coded csvs
degree_dict = create_conversion_dict(
config.PERSISTENCE_PATH + '/code/etl/mappers/degreetranslation.csv')
major_dict = create_conversion_dict(
config.PERSISTENCE_PATH + '/code/etl/mappers/majortranslation.csv')
# do conversions from freetext to coded options
clean_enrollment['degree_subject'] = enrollment.Degree_Text__c.fillna(
'missing').apply(lambda x: convert_free_text(degree_dict, x))
clean_enrollment['major'] = enrollment.Major_Text__c.fillna(
'missing').apply(lambda x: convert_free_text(major_dict, x))
# convert withdrawal reasons to several boolean categories
def boolean_withdrawal(dummy_code, input_code):
if input_code == 'missing':
return np.nan
else:
codes = input_code.split(';')
dummy_booleans = [
True if code == dummy_code else False for code in codes
]
if sum(dummy_booleans) > 0:
return True
else:
return False
withdrawal_codes = [
'Financial', 'Academic', 'Motivational', 'Family', 'Health', 'Social',
'Racial Conflict'
]
for dummy_code in withdrawal_codes:
clean_enrollment['withdrawal_reason_' + "_".join(dummy_code.lower(
).split())] = enrollment.Withdrawal_code__c.fillna('missing').apply(
lambda x: boolean_withdrawal(dummy_code, x))
clean_enrollment.rename(columns={
'withdrawal_reason_racial_conflict':
'withdrawal_reason_racial'
},
inplace=True)
# Return clean df
return clean_enrollment
def enrollments_table(kipp_nj_enrollments):
'''Cleans KIPP NJ enrollment data to match our database schema'''
# remove the did not enroll, other, and deferred enrollment types
clean_enrollments = kipp_nj_enrollments[~kipp_nj_enrollments.status.isin(
['Other', 'Did Not Enroll', 'Deferred'])]
# rename Withdrawn to withdrew, matriculated to matriculating
clean_enrollments.status = clean_enrollments.status.apply(
lambda x: map_value_from_dict(status_fixed_mapping, x))
# made dates into dates
clean_enrollments.start_date = pd.to_datetime(clean_enrollments.start_date)
clean_enrollments.end_date = pd.to_datetime(clean_enrollments.end_date)
clean_enrollments.date_last_verified = pd.to_datetime(
clean_enrollments.date_last_verified)
# deal with degree type
#remove high school diploma and GED, only interested in college enrollments
clean_enrollments = clean_enrollments[~clean_enrollments.degree_type.
isin(['High School Diploma', 'GED'])]
clean_enrollments.degree_type = clean_enrollments.degree_type.apply(
lambda x: map_value_from_dict(degree_fixed_mapping, x))
# clean up degree subject
clean_enrollments.degree_subject = clean_enrollments.degree_type.combine(
clean_enrollments.degree_subject, func=code_degree_subject)
# clean up major
major_dict = create_conversion_dict(
config.PERSISTENCE_PATH + '/code/etl/mappers/majortranslation.csv')
# do conversions from freetext to coded options
clean_enrollments['major'] = clean_enrollments.major.fillna(
'missing').apply(lambda x: convert_free_text(major_dict, x))
#map transfer reasons to the reasons we keep track of
withdrawal_reasons = pd.get_dummies(
clean_enrollments.transfer_reason__c).astype(bool)
withdrawal_reasons.columns = [
'withdrawal_reason_academic', 'withdrawal_reason_career',
'withdrawal_reason_financial', 'withdrawal_reason_other',
'withdrawal_reason_placement', 'withdrawal_reason_relocation',
'withdrawal_reason_social'
]
withdrawal_reasons.drop([
'withdrawal_reason_relocation', 'withdrawal_reason_placement',
'withdrawal_reason_other', 'withdrawal_reason_career'
],
axis=1,
inplace=True)
# join the columns back into the original
clean_enrollments = clean_enrollments.join(withdrawal_reasons)
clean_enrollments['withdrawal_reason_motivational'] = np.nan
clean_enrollments['withdrawal_reason_family'] = np.nan
clean_enrollments['withdrawal_reason_health'] = np.nan
clean_enrollments['withdrawal_reason_racial'] = np.nan
# Drop invalid IPEDS id
clean_enrollments['college_ncesid'] = clean_enrollments[
'college_ncesid'].convert_objects(convert_numeric=True)
clean_enrollments.loc[clean_enrollments['college_ncesid'] > 999999,
'college_ncesid'] = np.nan
clean_enrollments['college_ncesid'] = clean_enrollments[
'college_ncesid'].apply(int_with_NaN_tostr)
clean_enrollments.drop(['transfer_reason__c', 'college_salesforce_id'],
axis=1,
inplace=True)
clean_enrollments.rename(columns={
'student_salesforce_id': 'kipp_nj_sf_id',
'college_ncesid': 'ipedsid'
},
inplace=True)
return clean_enrollments
