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#!/usr/bin/python3
import sys
import pandas as pd
import numpy as np
import os
# infile = 'wa.csv'
infile = sys.argv[1]
f = open(infile)
l= f.readlines()
h= l[0]
hsub=l[1] # TheExpertTA keep headers in 2 lines (mixed with max possible points)
maxPossible=l[1]
f.close()
# clean up of headers
h=h.strip()
h = h.replace('"', '')
hsub = hsub.strip()
hsub = hsub.replace('"', '')
hsub = hsub.split(',')
headers = h.split(',')
# we should fail hard if this column namess are not present
headers[hsub.index('Last')]='LastName'
headers[hsub.index('First')]='FirstName'
headers[hsub.index('Email')]='UserName'
headers[hsub.index('Student No')]='SID'
# headers[0]='FullName'
# headers[1]='UserName'
# headers[2]='SID'
# headers[3]='TotalPcnt'
# headers[4]='TotalScore'
d = pd.read_csv(infile, skiprows=[0,1], header=None, names=headers)
# d.loc[0, 'FullName']='MaxScore'
# d.loc[0, 'UserName']='MaxScore'
# cleanup
# c = d.columns
# c=c.drop(['FullName', 'UserName', 'SID'])
# index = d[c] == 'ND'
# d[index] = np.nan
# index = d[c] == 'NS'
# d[index] = np.nan
# TheExperTA last column contains 'Averages' per student which we do not need
d.drop(columns=['Averages'], inplace=True)
# TheExperTA last row contains Averages per assignment which we do not need
row = d[(d['LastName']=='Averages') & (d['FirstName'].isna()) & (d['UserName'].isna())]
d.drop(row.index, inplace=True)
# hand tuned fixes
# d['UserName'].replace('phanng@hotmail.com@tj.va$', 'kphan@wm.edu', regex=True, inplace=True)
d.to_csv('TheExperTA.csv')
# now import to sqlite3
os.popen('rm -f TheExperTA.db')
p = os.popen('printf ".mode csv\n.import \"TheExperTA.csv\" export_table\n.q" | sqlite3 TheExperTA.db')
p.close()
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