An Insightful Story

I read this really insightful story somewhere.
I am reproducing it, though not verbatim.

In a make-you-successful-workshop, the guide took a glass in hand poured water into it. Now the glass was half full.
The attendes thought they knew what question was coming, Is the glass half full or half empty?, but they were wrong.
The guide asked what is the weight of the glass.
The audience approxiamated and answered something between 8oz to 20oz.
Ththe instructor told them that all the answers were wrong, and the question needed to be more specific to be answered, weight of the glass would vary with time.

Say if you have to pick it for a moment and then put it down you wont feel the weight. But you have to pick it up and keep it up for 5 minutes, you might feel some strain and the glass will appear to have considerable weight.Now if you have to keep it up for over an hour your hands might get numb.
Same happens when you think about something. If you think for a moment and then leave it , it does not weigh anything to you.
But if  you keep thinking about it for long it becomes heavier and heavier .Heavy enough to crush you.
So how can you avoid being crushed?
When an issue bugs you and forces you to think about it for the first time take action so you dont have to think about it for long.

Hold the glass only while you drink from it!

CUCET CUJ Results

So Central University of Jharkhand [CUJ] has posted the scores of people who have applied for the various programs.
Mind it they have not posted the results yet, and I think that is because they are confused about how much weightage should be given to Board marks. JEE(Mains) decision has already caused havoc to sincere people, Lets hope the same does not happen here.
So they [CUJ] posted the list of applied candidates in pdf and did not do the totalling of 5 subjects leaving people with raw data.Maybe they did not want you to know your rank.
So I started to extract the result from three pdf files.
Pdf to excel convertors have not worked for me  till date and they failed again.
So i opened the pdf in foxit reader and selected “view as text files”, a neat feature with which you can copy paste anything to a text file. But remember of saving it in Unicode not Ansi which is default.Notepad will warn you though.
And copied the 3 lists into one text file.
After cleaning the data and using ” ” as the separator I was ready to pase the data.
With some knowledge of python that I had I wrote a python script that parsed the text file and gave the output.
Here is the rank list of all the people who have applied for MBA program at CUJ.

S R Roll  Name       Sco  Board  #S=Serial No,#R=Rank,#Roll,#Sco=Marks in CUCET,#Board=Board score
Total appeared: 406

1 1 103518 RAVI 80.0 
 OBC
2 2 1533 VAIBHAV 78.0 66.2 Gen
3 2 28369 SANDEEP 78.0 60.0 Gen
4 3 9023 ROHAN 75.0 79.0 Gen
5 3 14643 AKANCHHA 75.0 81.6 OBC
6 4 3966 AASTHA 74.0 88.8 Gen
7 5 38753 SHUBHA 71.0 69.0 Gen
8 6 100448 Rishabh 70.0 60.0 GE
9 7 1205 SACHIN 69.0 78.6 Gen
10 8 101297 Ajita 68.0 
 GE
11 8 100782 KESHAV 68.0 75.2 OBC
12 8 100341 SONAM 68.0 78.0 OBC
13 8 102663 Anurag 68.0 82.0 SC
14 9 31812 DIKSHA 67.0 67.6 Gen
15 9 100458 NANDINI 67.0 67.6 GE
16 9 14635 PRIYA 67.0 77.4 Gen
17 9 104804 Sharathkumar 67.0 51.66 OBC
18 10 13792 RASHMI 66.0 67.67 Gen
19 10 17370 RAJ 66.0 88.2 Gen
20 10 1861 AKANKSHA 66.0 74.6 Gen
21 10 103666 Aakansha 66.0 74.2 ST
22 11 100399 VIVEK 65.0 82.2 GE
23 11 102778 VINAY 65.0 
 OBC
24 11 12620 VISHWAJEET 65.0 70.6 OBC
25 12 100032 BHARGAVA 64.0 67.0 GE
26 12 101436 SANDEEP 64.0 
 GE
27 12 35769 KUMAR 64.0 71.0 Gen
28 13 103144 LAKSHIT 63.0 81.2 GE
29 13 17988 KUMARI 63.0 81.2 Gen
30 13 23694 BITTU 63.0 66.4 Gen
31 13 100472 PUSHPITA 63.0 
 OBC
32 14 105584 ROUSHAN 62.0 
 GE
33 14 121212 PRASHUN 62.0 61.0 GE
34 14 102607 Deepanwita 62.0 
 GE
35 14 103095 ravi 62.0 73.4 GE
36 14 33279 PRANAV 62.0 60.8 Gen
37 14 4788 TIPPANA 62.0 66.2 Gen
38 14 100780 PULUMATI 62.0 71.0 GE
39 14 101113 Sabita 62.0 62.0 OBC
40 14 18422 MOHIT 62.0 77.2 OBC
41 14 101657 Chandu 62.0 
 SC
42 15 102556 ASHWINI 61.0 
 OBC
43 16 5666 ANAND 60.0 64.0 Gen
44 16 9603 TAANAY 60.0 60.8 Gen
45 16 1984 ANURADHA 60.0 86.0 OBC
46 16 12266 SACHINDRA 60.0 63.2 OBC
47 16 34276 SAKSHI 60.0 92.0 OBC
48 16 18882 CHANDAN 60.0 66.4 SC
49 17 7686 UDBHAW 59.0 75.67 Gen
50 17 100807 R 59.0 
 GE
51 17 10531 KUNWAR 59.0 77.0 Gen
52 17 30819 SAKET 59.0 81.67 Gen
53 17 8834 PRAKASH 59.0 63.4 Gen
54 17 100831 Kunal 59.0 55.6 OBC
55 17 28156 AKHIL 59.0 55.8 ST
56 18 7764 AKANKSHA 58.0 81.0 Gen
57 18 101407 Abhijeet 58.0 60.0 GE
58 18 19291 ABHISHEK 58.0 55.8 OBC
59 19 34049 AISHWARY 57.0 66.2 Gen
60 19 4321 SHUBHAM 57.0 80.0 Gen
61 19 12775 RAJAN 57.0 60.6 OBC
62 19 8584 OM 57.0 81.6 OBC
63 19 19410 ASHWANI 57.0 61.2 SC
64 20 121212 Shivangi 56.0 70.4 GE
65 20 100890 ANIRBAN 56.0 75.33 OBC
66 20 15464 UJJWAL 56.0 66.6 OBC
67 20 101148 Neha 56.0 61.0 OBC
68 20 8021 MONI 56.0 87.0 OBC
69 21 21627 ADITI 55.0 56.8 Gen
70 21 102803 Chandan 55.0 62.0 OBC
71 22 101681 PRIYANKA 54.0 51.6 GE
72 22 100823 ANURAG 54.0 
 GE
73 22 4953 MOHAMMAD 54.0 67.4 Gen
74 22 103187 Parth 54.0 
 GE
75 22 104767 Nilesh 54.0 58.2 OBC
76 22 103285 ADARSH 54.0 
 OBC
77 22 100769 ABHISHEK 54.0 68.8 OBC
78 22 19110 DIVYA 54.0 72.2 OBC
79 22 27334 DEEPU 54.0 57.8 ST
80 23 18851 SARANJITA 53.0 62.6 Gen
81 23 27351 SHIVAM 53.0 63.4 Gen
82 23 27696 KUMARI 53.0 81.0 Gen
83 23 29531 KARTIKEYA 53.0 77.6 Gen
84 23 30068 NAHID 53.0 57.0 Gen
85 23 4666 SHALINI 53.0 70.6 Gen
86 23 104157 Mohit 53.0 63.16 GE
87 23 11337 KUMAR 53.0 55.8 OBC
88 23 21407 DIKSHA 53.0 64.6 OBC
89 23 34691 SANTOSH 53.0 62.6 OBC
90 23 38157 ANUPAMA 53.0 52.8 ST
91 24 4113 SURBHI 52.0 71.8 Gen
92 24 102463 Ravi 52.0 65.2 GE
93 24 101784 Sonali 52.0 56.0 OBC
94 25 101809 RIYA 51.0 
 GE
95 25 19577 PRATIK 51.0 82.4 Gen
96 25 22464 PRAKHAR 51.0 58.8 Gen
97 25 23737 SHUBHAM 51.0 61.4 Gen
98 25 18681 SUSHMITA 51.0 80.0 Gen
99 25 101594 Shusant 51.0 60.0 GE
100 25 21102 RAHUL 51.0 74.0 OBC
101 25 101290 SAKSHI 51.0 62.0 OBC
102 25 104140 Neha 51.0 
 ST
103 25 37278 VISHAL 51.0 52.0 SC
104 25 7768 SWAPNIL 51.0 58.6 SC
105 26 121221 DIKSHA 50.0 62.0 GE
106 26 104688 Neha 50.0 74.4 GE
107 26 34466 PRIYANKA 50.0 75.2 Gen
108 26 4895 ANUSHRUTI 50.0 77.0 Gen
109 26 103937 Satyam 50.0 69.0 GE
110 26 100299 SHRIYA 50.0 67.0 GE
111 26 100933 GOLDEN 50.0 58.0 OBC
112 26 101464 BALVIND 50.0 70.6 OBC
113 26 103282 AKSHAY 50.0 63.0 OBC
114 26 103538 PGOUTHAM 50.0 80.6 OBC
115 27 21018 AMAN 49.0 57.4 Gen
116 27 24715 KARISHMA 49.0 64.8 Gen
117 27 25964 SIMRAN 49.0 73.8 Gen
118 27 102490 VEMULURI 49.0 70.6 GE
119 27 100841 AYUSH 49.0 
 GE
120 27 29907 RUDRADUTT 49.0 63.0 Gen
121 27 101534 AMAR 49.0 
 GE
122 27 34629 RISHI 49.0 71.2 Gen
123 27 32717 MAYANK 49.0 72.8 OBC
124 27 103356 KUMAR 49.0 
 OBC
125 27 102838 KAUSHAL 49.0 67.33 OBC
126 27 103223 vikash 49.0 
 OBC
127 27 25416 VISHAL 49.0 68.6 OBC
128 27 35184 BITTU 49.0 60.0 OBC
129 27 101717 SHALINI 49.0 
 ST
130 27 10789 MONALI 49.0 49.2 SC
131 27 104620 SATYAM 49.0 81.4 SC
132 28 101177 AMAN 48.0 58.2 GE
133 28 101319 ABHIJEET 48.0 
 GE
134 28 101352 MALAY 48.0 66.0 GE
135 28 104563 VIVEK 48.0 69.2 GE
136 28 100846 Sweta 48.0 65.0 GE
137 28 15565 SATYA 48.0 63.38 Gen
138 28 21538 KUMAR 48.0 66.4 Gen
139 28 22829 KUMARI 48.0 52.2 Gen
140 28 32137 ROHIT 48.0 74.2 Gen
141 28 34444 ANURAG 48.0 81.8 Gen
142 28 23396 SHIVA 48.0 66.8 Gen
143 28 26670 HARSHA 48.0 71.6 Gen
144 28 9890 AVISHEK 48.0 77.2 Gen
145 28 101374 alok 48.0 
 GE
146 28 100384 AFSHA 48.0 87.0 GE
147 28 26429 ANIL 48.0 54.4 OBC
148 28 101384 Monika 48.0 53.0 ST
149 29 101251 SAKSHI 47.0 
 GE
150 29 101835 sumit 47.0 
 GE
151 29 101939 RITESH 47.0 57.33 GE
152 29 105537 SURBHI 47.0 
 GE
153 29 8091 PRIYANKA 47.0 54.4 Gen
154 29 101950 SUBHRATA 47.0 66.8 GE
155 29 101785 Tanmaya 47.0 58.0 GE
156 29 100119 amrutha 47.0 
 GE
157 29 33359 PRAGYA 47.0 61.67 Gen
158 29 1137 RISHI 47.0 67.2 OBC
159 29 6199 RAHUL 47.0 60.6 OBC
160 29 102740 Naiyara 47.0 
 OBC
161 29 100348 RAJKUMAR 47.0 56.8 OBC
162 29 13550 AANCHAL 47.0 58.25 OBC
163 29 20802 PRADEEP 47.0 64.2 OBC
164 29 21049 VINAY 47.0 79.2 OBC
165 29 103325 BIPIN 47.0 55.6 ST
166 30 102311 NIDHI 46.0 67.4 GE
167 30 102686 RUPALI 46.0 74.2 GE
168 30 104459 HARSH 46.0 56.8 GE
169 30 1568 RUPAM 46.0 69.0 Gen
170 30 16889 ANKITA 46.0 67.6 Gen
171 30 20203 RASHMI 46.0 72.0 Gen
172 30 23370 SUBHAM 46.0 67.2 Gen
173 30 28721 SWATI 46.0 70.17 Gen
174 30 30629 ALOK 46.0 69.4 Gen
175 30 101305 GOVIND 46.0 62.8 OBC
176 30 11984 ASHISH 46.0 60.4 BC
177 30 101705 Medini 46.0 
 OBC
178 30 17347 PRIYANKA 46.0 58.4 OBC
179 30 4440 KAUSER 46.0 67.4 OBC
180 31 100922 NEHA 45.0 
 GE
181 31 101110 CHANCHALA 45.0 60.0 GE
182 31 103065 ASHISH 45.0 
 GE
183 31 15990 SHIVESH 45.0 61.6 Gen
184 31 37618 PREETI 45.0 75.8 Gen
185 31 20855 TANVI 45.0 84.0 Gen
186 31 21048 ASHLESHA 45.0 62.67 Gen
187 31 3476 MITALI 45.0 67.6 Gen
188 31 25640 DIVIN 45.0 73.83 Gen
189 31 28066 ASHWINI 45.0 82.83 Gen
190 31 103174 DIVYA 45.0 
 OBC
191 31 31849 VINEETANJALI 45.0 71.8 OBC
192 31 16698 RAKESH 45.0 58.4 OBC
193 31 9820 JUHI 45.0 57.4 OBC
194 31 102529 Suraj 45.0 61.6 OBC
195 31 24436 KHUSHBOO 45.0 57.6 OBC
196 31 101058 sachin 45.0 
 ST
197 31 101580 TWINKLE 45.0 70.8 ST
198 31 103567 SONALI 45.0 59.6 SC
199 32 101399 Naincy 44.0 69.6 GE
200 32 11615 PIYUSH 44.0 66.8 Gen
201 32 14002 SUDEEP 44.0 55.4 Gen
202 32 19078 SAJAN 44.0 70.0 Gen
203 32 2723 DEEPTI 44.0 56.8 Gen
204 32 4972 MEENAKSHI 44.0 73.6 Gen
205 32 104482 BISHWAS 44.0 67.6 GE
206 32 103232 DEEPAK 44.0 
 OBC
207 32 100806 Priyanka 44.0 65.0 OBC
208 32 4800 SHAMSHAD 44.0 55.0 OBC
209 32 100289 MAHESH 44.0 52.6 OBC
210 32 105607 samarth 44.0 62.0 OBC
211 32 100556 LOVELY 44.0 
 OBC
212 32 10248 SWATI 44.0 60.4 OBC
213 32 102978 SWETA 44.0 68.0 SC
214 32 103170 Brajesh 44.0 63% SC
215 33 101379 SONALIKA 43.0 67.2 GE
216 33 103255 Surbhi 43.0 69.0 GE
217 33 11724 PRIYANKA 43.0 50.4 Gen
218 33 102189 RANI 43.0 
 GE
219 33 100809 DIGVIJAY 43.0 52.0 GE
220 33 101406 Manikant 43.0 54.2 GE
221 33 101722 PRIYANKA 43.0 71.6 GE
222 33 13708 UNIKHIL 43.0 76.42 Gen
223 33 12728 AYUSHI 43.0 80.8 OBC
224 33 102281 BHOYAR 43.0 
 OBC
225 33 101618 ADITYA 43.0 68.4 OBC
226 33 104438 TANYA 43.0 95.0 OBC
227 33 100467 ANIKET 43.0 57.0 OBC
228 33 100626 DHARMENDRA 43.0 57.4 OBC
229 33 12364 NIDHI 43.0 72.83 OBC
230 33 25208 RUCHI 43.0 61.4 OBC
231 33 37575 ANUJ 43.0 52.8 OBC
232 33 100456 SUNITA 43.0 
 ST
233 33 35228 DIPESH 43.0 63.2 ST
234 34 101114 ABHIJIT 42.0 66.0 GE
235 34 102486 ANIRBAN 42.0 58.0 GE
236 34 100194 DIVYA 42.0 70.0 GE
237 34 15167 SNIGDHA 42.0 56.0 Gen
238 34 17374 NEHA 42.0 70.0 Gen
239 34 35498 SAGAR 42.0 62.4 Gen
240 34 103134 Awanish 42.0 
 GE
241 34 104186 PRINCE 42.0 77.6 GE
242 34 100858 SAKSHI 42.0 
 GE
243 34 105643 SAHIL 42.0 60.0 OBC
244 34 16828 SANTOSH 42.0 66.4 OBC
245 34 36442 ANIMESH 42.0 47.4 OBC
246 34 105651 KISHAN 42.0 75.0 OBC
247 34 100905 NIKITA 42.0 58.2 OBC
248 34 102634 anshu 42.0 
 OBC
249 34 100696 SHABNAM 42.0 
 OBC
250 34 20283 NEHA 42.0 68.8 OBC
251 34 37620 PARIKSHIT 42.0 58.2 OBC
252 35 101228 Anurag 41.0 61.5 GE
253 35 103962 RISHU 41.0 
 GE
254 35 29377 ABHINAV 41.0 67.2 Gen
255 35 36681 HIMANSHU 41.0 61.8 Gen
256 35 6223 NIKET 41.0 64.4 Gen
257 35 102914 SURABHI 41.0 
 GE
258 35 103492 Naman 41.0 62.8 GE
259 35 103537 sonal 41.0 65.4 GE
260 35 31573 NAVAKANSHI 41.0 78.0 Gen
261 35 101163 ABHIJEET 41.0 61.0 OBC
262 35 31168 GAUTAM 41.0 71.4 OBC
263 35 101494 MD 41.0 71.2 OBC
264 35 100423 Raj 41.0 63.2 OBC
265 35 1636 KAMAL 41.0 58.8 OBC
266 35 23221 VIJAY 41.0 65.2 OBC
267 35 25715 PRAJJWAL 41.0 76.2 OBC
268 35 103024 BONIFACE 41.0 
 ST
269 35 103215 RAHUL 41.0 
 SC
270 35 100196 DILEEP 41.0 60.0 SC
271 36 103101 Swagata 40.0 55.8 GE
272 36 100683 KUMARI 40.0 56.67 GE
273 36 14860 S 40.0 62.8 Gen
274 36 20875 AASHISH 40.0 56.8 Gen
275 36 28445 ROCKY 40.0 74.4 Gen
276 36 9567 SHIVANGI 40.0 57.2 Gen
277 36 23142 SUBHADEEP 40.0 68.0 Gen
278 36 102344 Swati 40.0 63.4 GE
279 36 102571 KILLIVALAVAN 40.0 73.5 OBC
280 36 103478 RAJENDRA 40.0 67.0 OBC
281 36 18883 MANOJ 40.0 52.0 OBC
282 36 105573 ANURADHA 40.0 60.02 OBC
283 36 18281 SHILPI 40.0 59.6 OBC
284 36 7696 LOVELY 40.0 62.0 OBC
285 36 101560 HIMANI 40.0 56.0 ST
286 36 100940 HIMANSHU 40.0 
 ST
287 37 100527 mohit 39.0 77.8 GE
288 37 19374 SHIVAM 39.0 58.2 Gen
289 37 22852 RAHUL 39.0 73.4 Gen
290 37 37728 DIVYA 39.0 60.46 Gen
291 37 101832 ASHISH 39.0 
 GE
292 37 102998 AMIT 39.0 
 GE
293 37 10570 PRABHAT 39.0 71.6 Gen
294 37 105650 PRAVIN 39.0 58.0 OBC
295 37 19547 KMPUSHPA 39.0 77.8 OBC
296 37 2129 GAURAV 39.0 55.8 OBC
297 37 103347 AMIT 39.0 
 OBC
298 37 100149 sony 39.0 68.2 OBC
299 37 100463 VIKASH 39.0 61.0 OBC
300 37 19145 SANDHYA 39.0 73.4 OBC
301 37 37632 VIGNESH 39.0 81.5 OBC
302 37 100365 UTTAM 39.0 
 SC
303 37 18410 RAGINI 39.0 53.6 SC
304 38 101010 SANDEEP 38.0 – GE
305 38 101433 shashank 38.0 65.6 GE
306 38 101856 SHASHI 38.0 66.4 GE
307 38 102670 rashmi 38.0 66.6 GE
308 38 102783 SHEFALI 38.0 69.4 GE
309 38 12349 SUPRIYA 38.0 61.6 Gen
310 38 2037 ROCKY 38.0 63.6 Gen
311 38 101045 nancy 38.0 64.14 GE
312 38 101085 RAJ 38.0 58.0 GE
313 38 100513 MANIK 38.0 64.6 GE
314 38 100836 sumit 38.0 58.0 GE
315 38 101634 YACHNA 38.0 59.0 OBC
316 38 31114 MRINAL 38.0 59.0 OBC
317 38 6139 KAFIL 38.0 59.8 OBC
318 38 100717 RAMESH 38.0 56.83 SC
319 39 101205 RUCHI 37.0 
 GE
320 39 100190 SHIKHA 37.0 
 GE
321 39 102722 AMAN 37.0 
 GE
322 39 100356 priyanka 37.0 64.2 GE
323 39 102678 Shikha 37.0 
 OBC
324 39 15890 KOMAL 37.0 69.8 OBC
325 39 105599 BARKHA 37.0 56.0 OBC
326 39 100000 Nibha 37.0 
 OBC
327 39 103502 kumar 37.0 67.2 OBC
328 39 103717 PIKESH 37.0 58.0 OBC
329 39 100461 RAHUL 37.0 
 OBC
330 39 40036 KHUSBOO 37.0 58.2 OBC
331 39 102400 SANDHYA 37.0 58.8 ST
332 39 100658 ANCHALI 37.0 54.2 ST
333 40 101607 ABHINEETA 36.0 52.0 GE
334 40 14312 RAHUL 36.0 58.2 Gen
335 40 6378 AJIT 36.0 51.6 Gen
336 40 28847 ANURAG 36.0 71.6 Gen
337 40 105536 SUMIT 36.0 
 GE
338 40 25033 SATYAM 36.0 62.0 Gen
339 40 103740 SHIKHA 36.0 70.0 OBC
340 40 102020 BHASKAR 36.0 
 OBC
341 40 102215 priyanka 36.0 60.0 OBC
342 40 13229 SHIVANGI 36.0 66.2 OBC
343 40 5250 KUNDAN 36.0 55.4 SC
344 41 102112 Shorya 35.0 58.6 GE
345 41 102498 KUMAR 35.0 
 GE
346 41 39188 NEHA 35.0 55.0 Gen
347 41 102003 SUSMITA 35.0 
 GE
348 41 3830 ANAMIKA 35.0 62.2 Gen
349 41 101814 SUSHMITA 35.0 59.0 OBC
350 41 14046 SAURABH 35.0 52.0 OBC
351 41 34121 TANUJ 35.0 62.0 ST
352 41 37207 RAVI 35.0 65.0 SC
353 41 100518 TIRUMAL 35.0 
 SC
354 42 102679 AKANKSHA 34.0 
 GE
355 42 101446 KHUSHBU 34.0 59.6 GE
356 42 100144 SURAJIT 34.0 52.0 GE
357 42 100174 NIRAJ 34.0 55.0 GE
358 42 102579 AMIT 34.0 
 OBC
359 42 16579 SONY 34.0 74.6 OBC
360 42 15340 FARIHA 34.0 56.2 OBC
361 42 102019 SWETA 34.0 
 OBC
362 42 18227 NATASHA 34.0 69.2 OBC
363 42 100263 GRASOM 34.0 57.4 ST
364 43 101093 shashi 33.0 55.8 GE
365 43 121212 SIMRAN 33.0 54.2 GE
366 43 102717 SHUBHAM 33.0 
 GE
367 43 121212 ANITA 33.0 
 GE
368 43 36878 SHRADDHA 33.0 72.2 Gen
369 43 103776 NITISH 33.0 
 GE
370 43 101791 ASHISH 33.0 
 OBC
371 43 102056 Sukanya 33.0 63.4 OBC
372 43 103041 Rohit 33.0 
 OBC
373 43 23935 ANJU 33.0 58.2 ST
374 44 103001 SUMEET 32.0 59.0 GE
375 44 16209 SHRISHTI 32.0 57.2 Gen
376 44 7054 ASHISH 32.0 66.4 Gen
377 45 101824 TANUSRI 31.0 
 GE
378 45 103355 Saurabh 31.0 
 GE
379 45 102970 KAJAL 31.0 58.8 GE
380 45 100027 PAWAN 31.0 
 OBC
381 45 100553 VIKASH 31.0 45.6 OBC
382 46 24239 KUNAL 30.0 56.2 Gen
383 46 101443 Afsana 30.0 60.0 OBC
384 46 10036 AVINASH 30.0 66.33 OBC
385 47 102039 ALOK 29.0 
 GE
386 47 20181 JOSWIL 29.0 49.8 ST
387 48 24062 ALKA 28.0 67.6 BC
388 48 103427 NANGUNURI 28.0 
 OBC
389 49 25339 NISHA 27.0 57.6 Gen
390 49 102842 DEEPU 27.0 
 GE
391 49 103644 SATISH 27.0 65.4 OBC
392 49 105610 amit 27.0 79.4 OBC
393 49 103637 RITESH 27.0 56.2 ST
394 50 102090 PRASHANT 26.0 
 GE
395 50 103468 BANDANA 26.0 
 GE
396 50 37002 MAHENDRA 26.0 71.2 OBC
397 50 101467 ROSHAN 26.0 58.4 OBC
398 51 104120 AKANKSHA 25.0 55.8 OBC
399 51 100523 CHANDAN 25.0 69.8 OBC
400 52 103612 PRAKASH 23.0 
 OBC
401 52 101633 RAVI 23.0 
 SC
402 53 30776 VIKASH 20.0 48.2 OBC
403 54 103886 Kumar 19.0 58.6 GE
404 55 103842 NITESH 8.0 
 GE
405 56 102521 ABHISHEK 7.0 69.0 GE
406 57 102250 MANORAMA 1.0 
 GE
You can see there are gaps at someplaces , ie due to the ppl who have not entered board marks yet, i am still learning python.

For some mysterious region when i tried to enter “N\A” for ppl with no board score I found that \n was entered instead.
In the above list preparation i have not given any weightage to board marks and the rank might change according to that.

Earlier i used dictionary and list for storing the data and sorting in place for finding the rank.
Afterwards i used sqlite3 module to use sqlite.
*You can use sqlite for storing the db in RAM using sqlite3.connect(“:memory:”) in python.
executing the sql is pretty simple using the sqlite connection cursor object call connection.execute(query) or connection.execute(query,variable)

I will post about the detail later.
Hope this helps the CUJ applicants:)

*UPDATE: posted list of all the categories unified

Learning To Ride

So we have reached here and we will ride it through and through.
We like to go over the hill and through the forests while it is still raining and is dark.
The animals won’t hurt us because we are animals too.
Let us do this as we can do nothing now.
Let us try and travel as far as we can.
This struggle will always continue.

Poems that I wrote 2: Chewing Gum

Chewing this mint
I get the hint
Aren’t some relations like chewing gum?

Attractively packed
We eye them greedily as promises stacked
Initially
Taste of confectionary
Mingled with smell of berry
You covet it as for cake topped with cherry
Or like the sweet sherry
Appetizing you for more….
As you start indulging in it
Its flavor mixes with your juices
Sort of adrenalin gushes
You are on a high
Chewing it slowly you fly……

Gradually softened
You chew
And chew
And chew
Taste buds hypnotized
Mind and heart mesmerized
Sweetness at its peak
But, fragrance turning bleak……..
Still you chew
But with time you utter ‘phew’
Saliva magic ends
The lovely tale bends
The sensation fades
Gum becomes gum
Drained of taste
Its waste!
Nothing seems fresh or new
And you realize ‘chewable’ (‘likeable’) people are few
Spitting out the gum
You regret your association with a bum
Out of self anger you spit feeling uncouth
As if to cleanse your mouth…….

Once again
Life turns stale
You miss the gale….

Looking around
Many people chewing the gum are found
Going through various stages
Enjoying, as if blessed by sages
You get jealous, bored and start looking for something new
Willing for another breakthrough
Hoping someone happens fast
And
Hoping the fun to last…
While my thinking and writing stint
Flavor has been lost by my mint
I too spit it out
But I don’t regret the money spent
Or the time that went
Because I am not tangled in such a mesh(mess)
Or
Because at least my mouth is minty fresh!!!

Poems that I wrote 1: Blackout

 

As the sight blurs,
The shores become less visible
As the wind carries my canoe away
The unknown is becoming known,
The fear inside me becomes more tangible
The phony confidence starts to sway
I find myself in jeopardy, it feels horrible!!
Reality, are you real?
Ohh terrible!
With wounds green and stabs umpteen
In this foggy sea, friends nor foes can be seen
Only me, the moisture and the salt
Can you tell who is at fault?
. . . . .
Pity! No one can.
Time or tide
None giving me a ride
Sigh!
..
..
.
.
Standing here
I still remember all the faces
I still remember all the places
The things they did
Their ugly intentions hid
The traitors masked as friends
Whom I could not defend
Their precipitancy rather verity made me weak
My energy and vigor lost in the leak
I sat baffled
As they ruffled……….
Finally they pushed me into this sea
Since then I and canoe is ‘we’…
..
.
……
It started to rain
And my body shook with pain
Somehow I manage to remain afloat
Helplessly on my broken boat
Sun, its aura is hazed
Stretch of sea, I am bewilderedly amazed
Rays of hope are onubilating
Ways to escape are terminating
Above and below, right and left
I see only water
My dreams of seeing land shatter
Now nothing seems to matter
Only canoe and me
We and sea…
Sea of hopelessness
Sea of abjectness
Sea of aggravations
And the
Sea of broken relations
……
Floating alone
Slowly, my senses turn to numb zone
Feelings fading
Vision blearing
The pain is muting
Black out!!
……

Thoughts: A not-so-poemish Poem

Thoughts
I have them
You have them
They have them

Thoughts
They come
They go
They take us places

Thoughts
They titilate
They excite
They engage

Thoughts
They make you bored
They make you mad
They make you sad

Thoughts
They were with you when you were not alone
They were with you when you were alone
They will be with you always

Thoughts
They have made you
They have helped you
They have shaped you

Thoughts
These are mine
Write down yours