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import csv
import pandas as pd
import numpy as np
import os
def write_to_csv_departments(time,teachingscore,teaching,courseContentscore,courseContent,
examinationscore,examination,labWorkscore,labWork,libraryFacilitiesscore,
libraryFacilities,extraCurricularscore,extraCurricular):
csv_file_path = 'dataset/database.csv'
df = pd.read_csv(csv_file_path)
# Creating a dictionary for the new row
new_row = {'Timestamp': time, 'teachingscore': teachingscore, 'teaching': teaching,
'coursecontentscore': courseContentscore, 'coursecontent': courseContent,
'examinationscore': examinationscore, 'examination': examination,
'labworkscore': labWorkscore, 'labwork': labWork, 'libraryfacilitiesscore': libraryFacilitiesscore,
'libraryfacilities': libraryFacilities, 'extracurricularscore': extraCurricularscore,
'extracurricular': extraCurricular, 'Email Address': ''}
# Convert the new row to a DataFrame
new_data = pd.DataFrame(new_row, index=[0])
# Append the new data to the existing DataFrame
df2 = pd.concat([df, new_data], ignore_index=True)
# Append the new data to the existing DataFrame
# df = df.append(new_data, ignore_index=True)
# print(df2)
df2.to_csv(csv_file_path, mode='w', index=False)
def write_to_csv_teachers(teacher1,teacher1score,teacher2,teacher2score,teacher3,teacher3score,
teacher4,teacher4score,teacher5,teacher5score,teacher6,teacher6score):
csv_file_path = 'dataset/teacherdb.csv'
# Read the existing headers
df = pd.read_csv(csv_file_path)
header = df.columns.tolist()
# Create a dictionary for the new row
new_row = {'teacher1': teacher1, 'teacher1score': teacher1score,
'teacher2': teacher2, 'teacher2score': teacher2score,
'teacher3': teacher3, 'teacher3score': teacher3score,
'teacher4': teacher4, 'teacher4score': teacher4score,
'teacher5': teacher5, 'teacher5score': teacher5score,
'teacher6': teacher6, 'teacher6score': teacher6score}
# Concat the new row to the DataFrame
df2 = pd.concat([df, new_data], ignore_index=True)
# Append the new data to the existing DataFrame
# df = df.append(new_data, ignore_index=True)
# print(df2)
df2.to_csv(csv_file_path, mode='w', index=False)
def get_counts():
csv_file_path = 'dataset/database.csv'
df = pd.read_csv(csv_file_path)
index = df.index
no_of_students = len(index)
total_feedbacks = len(index)*6
df1 = df.groupby('teachingscore').count()[['teaching']]
teaching_negative_count = df1['teaching'][-1]
teaching_neutral_count = df1['teaching'][0]
teaching_positive_count = df1['teaching'][1]
df1 = df.groupby('coursecontentscore').count()[['coursecontent']]
coursecontent_negative_count = df1['coursecontent'][-1]
coursecontent_neutral_count = df1['coursecontent'][0]
coursecontent_positive_count = df1['coursecontent'][1]
df1 = df.groupby('examinationscore').count()[['examination']]
examination_negative_count = df1['examination'][-1]
examination_neutral_count = df1['examination'][0]
examination_positive_count = df1['examination'][1]
df1 = df.groupby('labworkscore').count()[['labwork']]
labwork_negative_count = df1['labwork'][-1]
labwork_neutral_count = df1['labwork'][0]
labwork_positive_count = df1['labwork'][1]
df1 = df.groupby('libraryfacilitiesscore').count()[['libraryfacilities']]
libraryfacilities_negative_count = df1['libraryfacilities'][-1]
libraryfacilities_neutral_count = df1['libraryfacilities'][0]
libraryfacilities_positive_count = df1['libraryfacilities'][1]
df1 = df.groupby('extracurricularscore').count()[['extracurricular']]
extracurricular_negative_count = df1['extracurricular'][-1]
extracurricular_neutral_count = df1['extracurricular'][0]
extracurricular_positive_count = df1['extracurricular'][1]
total_positive_feedbacks = teaching_positive_count + coursecontent_positive_count + examination_positive_count + labwork_positive_count + libraryfacilities_positive_count + extracurricular_positive_count
total_neutral_feedbacks = teaching_neutral_count + coursecontent_neutral_count + examination_neutral_count + labwork_neutral_count + libraryfacilities_neutral_count + extracurricular_neutral_count
total_negative_feedbacks = teaching_negative_count + coursecontent_negative_count + examination_negative_count +labwork_negative_count + libraryfacilities_negative_count + extracurricular_negative_count
li = [teaching_positive_count,teaching_negative_count,teaching_neutral_count,
coursecontent_positive_count,coursecontent_negative_count,coursecontent_neutral_count,
examination_positive_count,examination_negative_count,examination_neutral_count,
labwork_positive_count,labwork_negative_count,labwork_neutral_count,
libraryfacilities_positive_count,libraryfacilities_negative_count,libraryfacilities_neutral_count,
extracurricular_positive_count,extracurricular_negative_count,extracurricular_neutral_count]
return no_of_students,\
int(round(total_positive_feedbacks / total_feedbacks * 100)),\
int(round(total_negative_feedbacks / total_feedbacks * 100)),\
int(round(total_neutral_feedbacks / total_feedbacks * 100)),\
li
def get_tables():
csv_file_path = 'dataset/database.csv'
df = pd.read_csv(csv_file_path)
df = df.tail(5)
return [df.to_html(classes='data')]
def get_titles():
csv_file_path = 'dataset/database.csv'
df = pd.read_csv('dataset/database.csv')
return df.columns.values
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