Recall the contents of the qbdata.txt file.
Colt McCoy QB CLE 135 222 1576 6 9 60.8% 74.5 Josh Freeman QB TB 291 474 3451 25 6 61.4% 95.9 Michael Vick QB PHI 233 372 3018 21 6 62.6% 100.2 Matt Schaub QB HOU 365 574 4370 24 12 63.6% 92.0 Philip Rivers QB SD 357 541 4710 30 13 66.0% 101.8 Matt Hasselbeck QB SEA 266 444 3001 12 17 59.9% 73.2 Jimmy Clausen QB CAR 157 299 1558 3 9 52.5% 58.4 Joe Flacco QB BAL 306 489 3622 25 10 62.6% 93.6 Kyle Orton QB DEN 293 498 3653 20 9 58.8% 87.5 Jason Campbell QB OAK 194 329 2387 13 8 59.0% 84.5 Peyton Manning QB IND 450 679 4700 33 17 66.3% 91.9 Drew Brees QB NO 448 658 4620 33 22 68.1% 90.9 Matt Ryan QB ATL 357 571 3705 28 9 62.5% 91.0 Matt Cassel QB KC 262 450 3116 27 7 58.2% 93.0 Mark Sanchez QB NYJ 278 507 3291 17 13 54.8% 75.3 Brett Favre QB MIN 217 358 2509 11 19 60.6% 69.9 David Garrard QB JAC 236 366 2734 23 15 64.5% 90.8 Eli Manning QB NYG 339 539 4002 31 25 62.9% 85.3 Carson Palmer QB CIN 362 586 3970 26 20 61.8% 82.4 Alex Smith QB SF 204 342 2370 14 10 59.6% 82.1 Chad Henne QB MIA 301 490 3301 15 19 61.4% 75.4 Tony Romo QB DAL 148 213 1605 11 7 69.5% 94.9 Jay Cutler QB CHI 261 432 3274 23 16 60.4% 86.3 Jon Kitna QB DAL 209 318 2365 16 12 65.7% 88.9 Tom Brady QB NE 324 492 3900 36 4 65.9% 111.0 Ben Roethlisberger QB PIT 240 389 3200 17 5 61.7% 97.0 Kerry Collins QB TEN 160 278 1823 14 8 57.6% 82.2 Derek Anderson QB ARI 169 327 2065 7 10 51.7% 65.9 Ryan Fitzpatrick QB BUF 255 441 3000 23 15 57.8% 81.8 Donovan McNabb QB WAS 275 472 3377 14 15 58.3% 77.1 Kevin Kolb QB PHI 115 189 1197 7 7 60.8% 76.1 Aaron Rodgers QB GB 312 475 3922 28 11 65.7% 101.2 Sam Bradford QB STL 354 590 3512 18 15 60.0% 76.5 Shaun Hill QB DET 257 416 2686 16 12 61.8% 81.3
We will now use this file as input in a program that will do some data processing. In the program, we will read each line of the file and print it with some additional text. Because text files are sequences of lines of text, we can use the for loop to iterate through each line of the file.
A line of a file is defined to be a sequence of characters up to and including a special character called the newline character. If you evaluate a string that contains a newline character you will see the character represented as \n. If you print a string that contains a newline you will not see the \n, you will just see its effects. When you are typing a Python program and you press the enter or return key on your keyboard, the editor inserts a newline character into your text at that point.
As the for loop iterates through each line of the file the loop variable will contain the current line of the file as a string of characters. The general pattern for processing each line of a text file is as follows:
for line in myFile: statement1 statement2 ...
To process all of our quarterback data, we will use a for loop to iterate over the lines of the file. Using thesplit method, we can break each line into a list containing all the fields of interest about the quarterback. We can then take the values corresponding to first name, lastname, and passer rating to construct a simple sentence.