Question

Hi it's python I imported a data which are so many words in txt and I arranged and reshaped with alphabetically both rows and columns

I was successful with these steps but I am stuck with next step

below is my code and screenshot

import numpy as np
import pandas as pd

data=pd.read_csv("/Users/superman/Downloads/words_file2.txt",header=None)
df_input=pd.DataFrame(data)
df_output=pd.DataFrame(np.arange(676).reshape((26,26)),
index = ['a','b','c','d','e','f','g','h','i','j','k','l','m','n','o','p','q','r','s','t','u','v','w','x','y','z'],
columns = ['a','b','c','d','e','f','g','h','i','j','k','l','m','n','o','p','q','r','s','t','u','v','w','x','y','z'])
df_output.index.name="Start"
df_output.columns.name="End"
df_output

This below screen shot is what I have to find

I have to find each word from input data frame (which is txt file) which its start and end character and add the count into the output data frame.

So for example let's say there's adbkqa , it starts with a and ends a so it counts 1 between start a and a end, so add 1 to the current value. another example which starts a and end z is not into the txt so it counts 0 like below screenshot.

Maybe my first step might be wrong (one condition is all data initially in this data frame are zeros) <- I don't get it so

please help me to get a correct answer like below code please help!

the txt file is here

https://drive.google.com/open?id=1VXtEPNBJ6ypJZ62ypeeWtzS9TjcGhBp4

End a b cd e f g h j..qrtuV Wxy z Start a 0 2 3 4 5 6 78 9 ...16 17 18 19 20 21 22 23 24 25 ь26 27 28 29 30 31 32 33 34 35 c 52 53 54 55 56 57 58 5960 61 d 78 79 80 8 82 83 84 85 86 87 42 43 44 45 46 4748 49 50 51 68 69 70 71 72 73 74 75 76 77 9495 96 97 98 99100 101 102 103 f 130 131 132 133 134 135 136 137 138 139 146 147 148 149 150 151 152 153 154 155 g 156 157 158159 160 161 162 163 164 165 172 173 174 175 176 177 178 179180 181 198 199 200 201 202 203 204 205 206 207 i 208 209 210 211 212 213 214 215 216 217 224 225 226 227 228 229 230 231 232 233 j 234 235 236 237 238 239 240 241 242 243 250 251 252 253 254 255 256 257 258 259 k 260 261 262 263 264 265 266 267268 269 ...276 277 278 279 280 281 282 283 284 285 295 302 303 304 305 306 307 308 309 310 311 m 312 313 314 315 316 317 318 319 320 321 ...328 329 330 331 332 333 334 335 336 337 n 338 339 340 341 342 343 344 345 346 347 354 355 356 357 358 359 360 361 362 363 o 364 365 366 367 368 369 370 371 372 373 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 406 407 408 409 410 411 412 413 414 415 q 416 417418 419 420 421 422 423 424 425 ...432 433 434 435 436 437 438 439 440 441 458 459 460 461 462 463 464 465 466 467 h 182 183 184 185 186 187 188 189 190 191 I 286 287 288 289 290 291 292 293 294 r 442 443 444 445 446 447 448 449 450451 End a b cd e fg h .q rstuv wxy z Start a 2 0 10 7 48 1 2 2 0 0.. 0 3 11 15 0 0 01 11 0 b 00 1 2 4 0 0 6 0 0.. 0 0 4 0 0 00 1 0 c1 0 1 5 38 0 410 0...0 3 14 21 0 0 00 13 0 d 0 0 3 5 36 0 1 3 00..0 4 6 12 0010 6 0 e 0 0 5 3 24 0 0 110..0 3 3 16 0010 7 0

Answer 1

CODE:

import numpy as np
import pandas as pd

data=pd.read_csv('words_file2.txt',header=None)

data.head()

df_output=pd.DataFrame(np.zeros(676,dtype=int).reshape((26,26)),
index = ['a','b','c','d','e','f','g','h','i','j','k','l','m','n','o','p','q','r','s','t','u','v','w','x','y','z'],
columns = ['a','b','c','d','e','f','g','h','i','j','k','l','m','n','o','p','q','r','s','t','u','v','w','x','y','z'])
df_output.index.name="Start"
df_output.columns.name="End"
df_output

#Here I have mentioned first i[-1] because in DataFrame first index goes for column
#so we go for End as we mentioned the Row name
for i in data[0]:
df_output[i[-1]][i[0]]+=1

df_output

OUTPUT:

In [15]: df output-pd.DataFrame (np.zeros (676,dtype-int).reshape( (26,26, index ['a', 'b','c','d','e','f','g','h','i','j','k','1','m','n','o','p','q','r','s','t','u','v','w','x','y','z"], columns ['a','b','c','d,"e','f','g','h','i','j','k','1','n','n','o','p','q','r','s','t','u','v','w','x','y','z']) df_ output.index.name-"Start" df_ output.columns.name-"End" df output out[15]: End a b cd e fg h i j. q r st uv w x y z Start b 0 0 0 0 0 0 0 00 0 ...0 0 0 0 0 0 0 0 0 0 9 0 0 0 00 000 0 0..000 0 00 0 0 0 0 j00 0 0 0 0 0 0 0 00 0000 0 0 0 00 m 0 0 0 0 0 0 0 000..0 0 0 0 0 0 0 0 00 n 0 0 0 0 0 0 0 00 0 ...0 0 0 0 0 0 0 0 0 0 P 0 0 0 00 0 0 0 0 0.000 0 00 0 0 0 0 q 0 0 0 00 000 0 0..000 0 00 0 0 0 0 u 0 0 0 0 0 0 0 000 ..0 0 0 0 0 0 0 0 0 0 w 0 0 0 0 0 0 0 00 0..0 000 0 0 0 0 0 у0000000000 0 0 0 0 0 0 0 0 0 0 26 rows x 26 columns

In [24]: #Here I have mentioned first ǐ[-1] because in DataFrame first index goes for column #50 we go for End as we mentioned the Row name for i in data[9]: df_output[i[-1]][i[0]]+-1 In [25]: df_output out [25]: End a b cd efg h i jq rs tuvw x y z Start a 2 010 7 49 1 2 2 0 00 3 11 15 0 0 0 1 11 0 b 0 0 1 4 0 0 6 0 00044 0 0 0 0 1 0 c 1 01 5 38 0 4 1 0 00 3 15 22 0 0 0 0 13 0 d 0 0 3 5 36 0 1 3 0 00 46 12 0 0 1 0 6 0 e 0 05 3 24 0 0 11 00 3 3 16 0 0 1 0 7 0 f 0 0 15 10 0 0 0 0 0...0 2 7 4 0 0 0 0 1 0 i0 0 12 42 0 0 0 0 00 3 11 18 0 0 0 0 3 0 j00 0 0 0 00 0 0 00 01 100 0 0 0 0 m 0 0 0 2 9 0 010 0...0192 0 0 0 0 2 0 n 0 0 1 0 5 0 0 0 0 00144 0 0 0 0 0 0 o 0 0 0 0 10 0 0 0 0 00 08 1 0 0 01 0 0 p 20 54 35 01 1 00 0 0 12 10 0 0 0 2 11 0 q 0 0 1 010000 0 .. 0 0 1 1 0 0 00 1 0 r 0 0 0 3 31 0 0 40 0...0 2 2 7 0 0 0 0 0 0 s 0 0 3 2 15 0 2 00 0..0 0 10 8 0 0 0 0 7 0 t 0 0 03 90 0 0 0 0 0 0 6 5 0 0 0 0 3 0 v 0 01 2 10 0 0 0 0 0015 2 0 0 01 3 0 w 0 0 0 2 0 010 001 0 0 0 0 1 0 1 0 y 0 0 001 00 0 000 0 0 000 0 0 00 26 rows x 26 columns

#If you have any queries feel free to comment