NEET (UG) 2024 result press release¶
NTA’s press release of 4 June 2024 announcing the NEET (UG) 2024 result prints nine statistics tables on pages 2-6:
|
Printed as |
Rows |
Columns |
|---|---|---|---|
|
1. Highlights of NEET (UG) - 2024 |
registered, present, nationality, gender, category, cities, centres … |
2019-2024 |
|
(unnumbered) 2023 vs 2024 |
total, gender, category |
2023, 2024, % change |
|
2. Language-wise |
13 languages |
2019-2024 |
|
3. Gender-wise |
Male, Female, Third gender, Total |
2023/2024 x registered, appeared, qualified |
|
4. Category-wise |
OBC, SC, ST, General, EWS, Total |
the same |
|
5. PwBD |
PwD |
the same |
|
6. Nationality-wise |
Indian, Foreign Nationals, NRI, OCI, Total |
the same |
|
7. Qualifying criteria |
UR/EWS … ST & PH, Total |
criteria, marks range and candidates per year |
|
8. State-wise (two pages) |
37 States/UTs, Others, Total |
2023/2024 x registered, appeared, qualified |
The rest of the release lists toppers by name; the recipe stops before them.
The files are in examples/nta_notice/: recipe.toml and nta_notice.py. The fixture is pages 2-6, in tests/fixtures/nta_notice/.
The recipe¶
name = "NEET (UG) 2024 result press release"
[pages]
match = 'Highlights of NEET|Registered\s+Appeared' # pages 2-6
[engines]
ocr = false
[normalize]
remove_commas = true # Table 1 prints 2,10,105 invigilators in 2024
[parser]
type = "custom"
function = "nta_notice.py:parse"
key = ["table", "row", "col"]
[[checks]]
column = "row"
rule = "Number of Candidates registered = Number of Candidates Present + Number of Candidates Absent"
by = ["col"]
where = "table == 'highlights'"
[[checks]]
column = "row"
rule = "Number of Candidates registered = Male + Female + Third Gender"
by = ["col"]
where = "table == 'highlights'"
# 2023 counts present candidates, hence >=; EWS starts in 2020
[[checks]]
column = "row"
rule = "Number of Candidates registered >= Un-Reserved + SC + ST + OBC + EWS"
by = ["col"]
where = "table == 'highlights' and col != '2019'"
missing = "fail"
[[checks]]
column = "row"
rule = "Total = rest"
by = ["table", "col"]
where = "table in ['gender', 'category', 'nationality', 'qualifying', 'state']"
[output]
layout = "table"
rows = ["table", "row"]
columns = "col"
format = "csv"
The table name is part of the key, so one run reads all nine tables and the checks pick a table with where. The output has one row per table row, tables one after another; each table fills its own columns.
Run it¶
pdfexorcist extract https://www.nta.ac.in/Download/Notice/Notice_20240604195244.pdf --recipe examples/nta_notice/recipe.toml -o neet2024.csv
pdftotext 615 readings 0.0s
pdfplumber 615 readings 0.5s
pymupdf 615 readings 0.0s
camelot 615 readings 1.5s
pdfium 613 readings 0.0s
Agreed 615 100.0% of cells
Unresolved 0
Failed checks 6 agreed values that break a rule
x Table 1: registered >= categories: 6 cells
table,row,2019,2020,2021,2022,2023,2024,change_pct,2023_registered,2023_appeared,2023_qualified,2024_registered,2024_appeared,2024_qualified,criteria,2023_marks,2024_marks
highlights,Number of Candidates registered,1519375,1597435,1614777,1872343,2087462,2406079,,,,,,,,,,
...
Each table, without the columns it leaves blank:
table,row,2019,2020,2021,2022,2023,2024
highlights,Number of Candidates registered,1519375,1597435,1614777,1872343,2087462,2406079
highlights,Un-Reserved,534072,475534,46-853,565964,592110,647260
highlights,Number of Cities,154,155,202,497,499,571
table,row,2023,2024,change_pct
change,Total Candidates,2059006,2406079,16.85
change,Gen-EWS,153363,190694,24.34
table,row,2019,2020,2021,2022,2023,2024
language,English,1204968,1263273,1265520,1476024,1672914,1892355
language,Malayalam,NA,NA,3031,1510,1003,958
table,row,2023_registered,2023_appeared,2023_qualified,2024_registered,2024_appeared,2024_qualified
gender,Third gender,13,11,3,18,17,10
category,EWS,154373,152197,98322,190700,186924,116229
pwbd,PwD,8037,7819,3508,9901,9514,4120
nationality,NRI,877,852,533,1304,1214,798
state,Others (including Outside India),6021,5858,4237,8277,7898,5638
state,Total,2087462,2038596,1145976,2406079,2333297,1316268
table,row,2023_qualified,2024_qualified,criteria,2023_marks,2024_marks
qualifying,UR/EWS,1014372,1165904,50th Percentile,720-137,720-164
qualifying,Total,1145976,1316268,,,
pdfexorcist show tests/fixtures/nta_notice/nta_notice_2024_p2-6.pdf --recipe examples/nta_notice/recipe.toml -o page.png draws what the recipe reads:
The parser¶
parse() in nta_notice.py, in outline (pseudocode):
def parse(pages):
table = None
for line in all lines:
words = the line's text, "46 - 853" joined to "46-853"
a header line ("CANDIDATES 2019 ...", "%age change", "Language 2019 ...",
"Gender Registered ...", "State Name", ...) -> table = its name; next line
label, values = the words, then the trailing values ("---", "NA" and "720-137" count)
PwD -> table "pwbd" (camelot drops that table's header)
name the row:
Table 1's three "Number of Candidates" rows wrap their last word: by order
Number of Cities: the first number under each year ("497 (including 14 ...")
the summary's unlabelled first row: Total Candidates
Table 7: "UR/EWS 50th Percentile" -> row UR/EWS, criteria 50th Percentile
skip the line unless it has its table's number of values, or the row was read before
yield {"table", "row", "col": the column's name, "value"} per value
the State table's Total ends the run: the top-100 list follows
What is unusual¶
Table 1 prints 2021 Un-Reserved as
46-853. pdfexorcist reads it as printed and the category check fails on 2021; 460853 makes the categories add up to registered.Table 1’s 2023 category column adds up to present candidates (2038596), not registered, so that check is
>=.The unnumbered summary’s 2023 total is 2059006, against 2087462 in Tables 1, 3, 4, 6 and 8, and its gender and category rows do not add up to its total in either year. Nothing there is checked.
The 2024 languages add up to 2406003, 76 fewer than registered.
tests/test_nta_notice.py runs the recipe on the fixture from the command line and compares every value with values typed from the rendered pages.