analytics.py

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