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added the conveter file for updated result format
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| Original file line number | Diff line number | Diff line change |
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| # Import the required Module | ||
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| import numpy as np | ||
| import pandas as pd | ||
| import pdfplumber | ||
| import csv | ||
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| # converting pdf to csv | ||
| def pdf_to_csv(input_pdf, output_csv): | ||
| with pdfplumber.open(input_pdf) as pdf: | ||
| with open(output_csv, 'w', newline='') as csv_file: | ||
| writer = csv.writer(csv_file) | ||
| for page in pdf.pages: | ||
| lines = page.extract_text().split('\n') | ||
| for line in lines: | ||
| writer.writerow([line]) | ||
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| input_pdf = "RN1.pdf" | ||
| output_csv = "output.csv" | ||
| pdf_to_csv(input_pdf, output_csv) | ||
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| df = pd.read_csv("output.csv") | ||
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| # Creating a DataFrame with one row of zeros for each column | ||
| df1 = pd.DataFrame({ | ||
| "Rollno" : [0], | ||
| "EM_ISE" : [0], "EM_ESE" : [0], "EM_THEORY_TOT" : [0], "EM_TW" : [0], | ||
| "SME_ISE" : [0], "SME_ESE" : [0], "SME_THEORY_TOT" : [0], "SME_TW" : [0], | ||
| "BEE_ISE" : [0], "BEE_ESE" : [0], "BEE_THEORY_TOT" : [0], "BEE_TW" : [0], | ||
| "EM1_ISE" : [0], "EM1_ESE" : [0], "EM1_THEORY_TOT" : [0], "EM1_TW" : [0], | ||
| "EP_ISE" : [0], "EP_ESE" : [0], "EP_THEORY_TOT" : [0], "EP_TW" : [0], | ||
| "BXE_ISE" : [0], "BXE_ESE" : [0], "BXE_THEORY_TOT" : [0], "BXE_TW" : [0], | ||
| "EC_ISE" : [0], "EC_ESE" : [0], "EC_THEORY_TOT" : [0], "EC_TW" : [0], | ||
| "PPS_ISE" : [0], "PPS_ESE" : [0], "PPS_THEORY_TOT" : [0], "PPS_TW" : [0], | ||
| "SGPA" : [0] | ||
| }) | ||
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| # for page 1 | ||
| # identifying the course | ||
| course_1 = df.iloc[11,0].split(' ')[0] | ||
| course_2 = df.iloc[27,0].split(' ')[0] | ||
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| # code for result 1 | ||
| if(course_1 == "101011-1"): | ||
| # index for separating the marks of subjects | ||
| a = df.iloc[11,0].split(' ') | ||
| b = df.iloc[9,0].split(' ') | ||
| c = df.iloc[12,0].split(' ') | ||
| d = df.iloc[13,0].split(' ') | ||
| e = df.iloc[14,0].split(' ') | ||
| f = df.iloc[15,0].split(' ') | ||
| g = df.iloc[16,0].split(' ') | ||
| h = df.iloc[17,0].split(' ') | ||
| i = df.iloc[18,0].split(' ') | ||
| j = df.iloc[19,0].split(' ') | ||
| k = df.iloc[20,0].split(' ') | ||
| l = df.iloc[23,0].split(' ') | ||
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| new = pd.DataFrame({ | ||
| "Rollno" : b[4], | ||
| "EM_ISE" :a[2] , "EM_ESE" :a[4] , "EM_THEORY_TOT" :a[9] , "EM_TW" : c[4], | ||
| "SME_ISE" :d[2] , "SME_ESE" :d[4] , "SME_THEORY_TOT" :d[9] , "SME_TW" :e[4] , | ||
| "BEE_ISE" :f[2], "BEE_ESE" :f[4], "BEE_THEORY_TOT" :f[9] , "BEE_TW" :g[4] , | ||
| "EM1_ISE" :h[2] , "EM1_ESE" :h[4] , "EM1_THEORY_TOT" :h[9] , "EM1_TW" : i[5], | ||
| "EP_ISE" :j[2] , "EP_ESE" :j[4], "EP_THEORY_TOT" : j[9], "EP_TW" : k[4], | ||
| "BXE_ISE" : [0], "BXE_ESE" :[0] , "BXE_THEORY_TOT" :[0] , "BXE_TW" : [0], | ||
| "EC_ISE" : [0], "EC_ESE" :[0] , "EC_THEORY_TOT" : [0], "EC_TW" : [0], | ||
| "PPS_ISE" :[0] , "PPS_ESE" : [0], "PPS_THEORY_TOT" : [0], "PPS_TW" : [0], | ||
| "SGPA" : l[4] }) | ||
| df1 = pd.DataFrame(pd.concat([df1,new],ignore_index = False)) | ||
|
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| if(course_1 == "102003-1"): | ||
| # index for separating the marks of subjects | ||
| a = df.iloc[11,0].split(' ') | ||
| b = df.iloc[9,0].split(' ') | ||
| c = df.iloc[12,0].split(' ') | ||
| d = df.iloc[13,0].split(' ') | ||
| e = df.iloc[14,0].split(' ') | ||
| f = df.iloc[15,0].split(' ') | ||
| g = df.iloc[16,0].split(' ') | ||
| h = df.iloc[17,0].split(' ') | ||
| i = df.iloc[18,0].split(' ') | ||
| j = df.iloc[19,0].split(' ') | ||
| k = df.iloc[20,0].split(' ') | ||
| l = df.iloc[23,0].split(' ') | ||
|
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| new = pd.DataFrame({ | ||
| "Rollno" : b[4], | ||
| "EM_ISE" :[0] , "EM_ESE" :[0] , "EM_THEORY_TOT" :a[0] , "EM_TW" : [0], | ||
| "SME_ISE" :a[2] , "SME_ESE" :a[4] , "SME_THEORY_TOT" :a[9] , "SME_TW" :c[4] , | ||
| "BEE_ISE" :[0], "BEE_ESE" :[0], "BEE_THEORY_TOT" :[0] , "BEE_TW" :[0] , | ||
| "EM1_ISE" :f[2] , "EM1_ESE" :f[4] , "EM1_THEORY_TOT" :f[9] , "EM1_TW" : g[5], | ||
| "EP_ISE" :[0] , "EP_ESE" :[0], "EP_THEORY_TOT" : [0], "EP_TW" : [0], | ||
| "BXE_ISE" : d[2], "BXE_ESE" :d[4] , "BXE_THEORY_TOT" :d[9] , "BXE_TW" : e[4], | ||
| "EC_ISE" : h[2], "EC_ESE" :h[4] , "EC_THEORY_TOT" : h[9], "EC_TW" : i[4], | ||
| "PPS_ISE" :j[2] , "PPS_ESE" : j[4], "PPS_THEORY_TOT" : j[9], "PPS_TW" : k[4], | ||
| "SGPA" : l[4] }) | ||
| df1 = pd.DataFrame(pd.concat([df1,new], ignore_index = False)) | ||
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| # code for result 2 | ||
| if(course_2 == "101011-1"): | ||
| # index for separating the marks of subjects | ||
| a = df.iloc[27,0].split(' ') | ||
| b = df.iloc[25,0].split(' ') | ||
| c = df.iloc[28,0].split(' ') | ||
| d = df.iloc[29,0].split(' ') | ||
| e = df.iloc[30,0].split(' ') | ||
| f = df.iloc[31,0].split(' ') | ||
| g = df.iloc[32,0].split(' ') | ||
| h = df.iloc[33,0].split(' ') | ||
| i = df.iloc[34,0].split(' ') | ||
| j = df.iloc[35,0].split(' ') | ||
| k = df.iloc[36,0].split(' ') | ||
| l = df.iloc[39,0].split(' ') | ||
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| new = pd.DataFrame({ | ||
| "Rollno" : b[4], | ||
| "EM_ISE" :a[2] , "EM_ESE" :a[4] , "EM_THEORY_TOT" :a[9] , "EM_TW" : c[4], | ||
| "SME_ISE" :d[2] , "SME_ESE" :d[4] , "SME_THEORY_TOT" :d[9] , "SME_TW" :e[4] , | ||
| "BEE_ISE" :f[2], "BEE_ESE" :f[4], "BEE_THEORY_TOT" :f[9] , "BEE_TW" :g[4] , | ||
| "EM1_ISE" :h[2] , "EM1_ESE" :h[4] , "EM1_THEORY_TOT" :h[9] , "EM1_TW" : i[5], | ||
| "EP_ISE" :j[2] , "EP_ESE" :j[4], "EP_THEORY_TOT" : j[9], "EP_TW" : k[4], | ||
| "BXE_ISE" : [0], "BXE_ESE" :[0] , "BXE_THEORY_TOT" :[0] , "BXE_TW" : [0], | ||
| "EC_ISE" : [0], "EC_ESE" :[0] , "EC_THEORY_TOT" : [0], "EC_TW" : [0], | ||
| "PPS_ISE" :[0] , "PPS_ESE" : [0], "PPS_THEORY_TOT" : [0], "PPS_TW" : [0], | ||
| "SGPA" : l[4] }) | ||
| df1 = pd.DataFrame(pd.concat([df1,new],ignore_index = False)) | ||
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| if(course_2 == "102003-1"): | ||
| # index for separating the marks of subjects | ||
| a = df.iloc[27,0].split(' ') | ||
| b = df.iloc[25,0].split(' ') | ||
| c = df.iloc[28,0].split(' ') | ||
| d = df.iloc[29,0].split(' ') | ||
| e = df.iloc[30,0].split(' ') | ||
| f = df.iloc[31,0].split(' ') | ||
| g = df.iloc[32,0].split(' ') | ||
| h = df.iloc[33,0].split(' ') | ||
| i = df.iloc[34,0].split(' ') | ||
| j = df.iloc[35,0].split(' ') | ||
| k = df.iloc[36,0].split(' ') | ||
| l = df.iloc[39,0].split(' ') | ||
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| new = pd.DataFrame({ | ||
| "Rollno" : b[4], | ||
| "EM_ISE" :[0] , "EM_ESE" :[0] , "EM_THEORY_TOT" :[0] , "EM_TW" : [0], | ||
| "SME_ISE" :a[2] , "SME_ESE" :a[4] , "SME_THEORY_TOT" :a[9] , "SME_TW" :c[4] , | ||
| "BEE_ISE" :[0], "BEE_ESE" :[0], "BEE_THEORY_TOT" :[0] , "BEE_TW" :[0] , | ||
| "EM1_ISE" :f[2] , "EM1_ESE" :f[4] , "EM1_THEORY_TOT" :f[9] , "EM1_TW" : g[5], | ||
| "EP_ISE" :[0] , "EP_ESE" :[0], "EP_THEORY_TOT" : [0], "EP_TW" : [0], | ||
| "BXE_ISE" : d[2], "BXE_ESE" :d[4] , "BXE_THEORY_TOT" :d[9] , "BXE_TW" : e[4], | ||
| "EC_ISE" : h[2], "EC_ESE" :h[4] , "EC_THEORY_TOT" : h[9], "EC_TW" : i[4], | ||
| "PPS_ISE" :j[2] , "PPS_ESE" : j[4], "PPS_THEORY_TOT" : j[9], "PPS_TW" : k[4], | ||
| "SGPA" : l[4] }) | ||
| df1 = pd.DataFrame(pd.concat([df1,new],ignore_index = False)) | ||
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| else: | ||
| print("Course not found") | ||
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| df1.to_csv("Output.csv",index = False) |