@@ -388,3 +388,194 @@ Home Restaurant - Los Feliz
388388
389389 ...
390390```
391+ 392+ # Writing Clean Data
393+ Once we have extracted the data we want to make our data look good i.e. not without
394+ any spaces and newlines so for that we will use a simple logic
395+ 396+ ** writing_clean_data.py**
397+ ```
398+ ...
399+ with open(file_path, 'a') as textFile:
400+ count = 0
401+ for biz in businesses:
402+ try:
403+ title = biz.find('a', {'class': 'biz-name'}).text
404+ address = biz.find('address').contents
405+ # print(address)
406+ phone = biz.find('span', {'class': 'biz-phone'}).text
407+ region = biz.find('span', {'class': 'neighborhood-str-list'}).contents
408+ count += 1
409+ for item in address:
410+ if "br" in item:
411+ print(item.getText())
412+ else:
413+ print('\n' + item.strip(" \n\r\t"))
414+ for item in region:
415+ if "br" in item:
416+ print(item.getText())
417+ else:
418+ print(item.strip(" \n\t\r") + '\n')
419+ ...
420+ ```
421+ 422+ We simply get the text of the item if there are any ** br** tags else we strip the
423+ newlines, return lines, tabs and spaces / space from the text, On running the file
424+ 425+ ```
426+ 800 W Sunset Blvd
427+ Echo Park
428+
429+
430+ 4156 Santa Monica Blvd
431+ Silver Lake
432+
433+
434+ 8500 Beverly Blvd
435+ Beverly Grove
436+
437+
438+ 5484 Wilshire Blvd
439+ Mid-Wilshire
440+
441+
442+ 5115 Wilshire Blvd
443+ Hancock Park
444+
445+
446+ 126 E 6th St
447+ Downtown
448+
449+
450+ 8164 W 3rd St
451+ Beverly Grove
452+
453+
454+ 7910 W 3rd St
455+ Beverly Grove
456+
457+
458+ 4163 W 5th St
459+ Koreatown
460+
461+
462+ 435 N Fairfax Ave
463+ Beverly Grove
464+
465+
466+ 1267 W Temple St
467+ Echo Park
468+
469+
470+ 429 W 8th St
471+ Downtown
472+
473+
474+ 724 S Spring St
475+ Downtown
476+
477+
478+ 8450 W 3rd St
479+ Beverly Grove
480+
481+
482+ 2308 S Union Ave
483+ University Park
484+
485+
486+ 5583 W Pico Blvd
487+ Mid-Wilshire
488+
489+ 'NoneType' object has no attribute 'contents'
490+
491+ 3413 Cahuenga Blvd W
492+ Hollywood Hills
493+
494+
495+ 727 N Broadway
496+ Chinatown
497+
498+
499+ 6602 Melrose Ave
500+ Hancock Park
501+
502+
503+ 612 E 11th St
504+ Downtown
505+ ...
506+ ```
507+ 508+ The same way we have to clean the phone number
509+ 510+ ```
511+ ...
512+ for item in phone:
513+ if "br" in item:
514+ phone_number += item.getText() + " "
515+ else:
516+ phone_number += item.strip(" \n\t\r") + " "
517+ ...
518+
519+ except Exception as e:
520+ print(e)
521+ logs = open('errors.log', 'a')
522+ logs.write(str(e) + '\n')
523+ logs.close()
524+ address = None
525+ phone_number = None
526+ region = None
527+ ```
528+ 529+ Again run the file and change the ``` start = 990 ``` delete all content in
530+ ``` yelp-{city}-clean.txt ``` run the file again. All details of the restaurant will
531+ be written to the file
532+ 533+ ** yelp-{city}-clean.txt**
534+ ```
535+ Tea Station Express
536+
537+
538+
539+
540+ Bestia
541+ 2121 E 7th Pl Downtown
542+ (213) 514-5724
543+
544+
545+ République
546+ 624 S La Brea Ave Hancock Park
547+ (310) 362-6115
548+
549+
550+ The Morrison
551+ 3179 Los Feliz Blvd Atwater Village
552+ (323) 667-1839
553+
554+
555+ A Food Affair
556+ 1513 S Robertson Blvd Pico-Robertson
557+ (310) 557-9795
558+
559+
560+ Running Goose
561+ 1620 N Cahuenga Blvd Hollywood
562+ (323) 469-1080
563+
564+
565+ Howlin’ Ray’s
566+
567+
568+
569+
570+ Perch
571+ 448 S Hill St Downtown
572+ (213) 802-1770
573+
574+
575+ Faith & Flower
576+ 705 W 9th St Downtown
577+ (213) 239-0642
578+ ```
579+ 580+ Notice that we some of the data is missing because due to some error we reduce the risk
581+ of code crashing by setting the values to ``` None ```
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