Welcome to the first post of Upwork Series. In this series, we are going to work on gig requirements posted on UpWork. Web Scraping & Data Extraction Our task is to create web crawler which will scrape data daily from the report pages of a transportation company. Click here to see description of project Step 1: Understanding the task First, it is important to understand the task clearly. They want from us to where each attribute listed above is its own separate column. scrape data then save it in CSV file We are going to crawl the following information: Date ("The information below reflects the content of the FMCSA management information systems as of {Date}") Operating Status Legal Name DBA Name Physical Address Mailing Address USDOT Number Power Units Drivers We can provide an initial set of 100 DOT numbers to make sure the information above can be scraped easily, from there we can provide all DOT numbers we would like to scrape on a daily basis. Report pages can be accessed by dots or with other name IDs. Each page has its own id (dot). So, these dots locate in Excel file. We have to read this file and extract dots from there then pass it into URL to access the report page. Step 2: Creating our environment and installing dependencies Now, we know what client wants from us, so let's create our virtual environment then inspect elements that we are going to crawl. To create virtualenv run the following command in your terminal: virtualenv env then install BeautifulSoup which is a Python package for parsing HTML and XML documents and xlrd which is a library for reading data and formatting information from Excel files: pip install beautifulsoup4 xlrd Step 3: Crawling Data Alright Devs! Let's start with opening the example url from project description so can see the fields. Click to see Example URL The page should look like this: Our first target is to find - Date ("The information below reflects the content of the FMCSA management information systems as of {Date}") The information below reflects the content of the FMCSA management information systems as of 01/01/2020. The truth is we can't crawl this element by specific class name or id. Unfortunately, this report pages messed up. But we can see that this element is in bold format. There are also many elements locate with the bold format. However, we can crawl all of them as text and use to extract the data we need. RegEx A regular expression (RegEx) is a special sequence of characters that helps you match or find other strings or sets of strings, using a specialized syntax held in a pattern. The date locates between and . So , to find the date regex will look between these strings. The information below reflects the content of the FMCSA management information systems as of .(dot) re urllib.request urllib.request urlopen, Request bs4 BeautifulSoup req = Request(url, headers={ : })
    html = urlopen(req).read()
    bs = BeautifulSoup(html, ) bold_texts = bs.find_all( ) b bold_texts: : date = re.search( , b.get_text(strip= , separator= )).group( ).strip() len(date) > :
                date = date.split( , )[ ]
            print(date) AttributeError: import # regex import from import from import : def crawl_data (url) 'User-Agent' 'Mozilla/5.0' 'html.parser' # Find all bold texts 'b' for in try # look between these strings 'The information below reflects the content of the FMCSA management information systems as of(.*).' True '  ' 1 # If regex finds multiple dots, extract string before first dot if 11 "." 1 0 except pass Well if you run the program you will see it's printing the date. Let me quickly show you how regex works, because I feel some of you trying to understand. Consider the following code: re

# We need to extarct the string
data = name = re.search( , data)
print(name)

# Output: import "coderasha" from "Hello my name is coderasha." 'Hello my name is (.*).' < ; = ), = > _sre.SRE_Match object span (0, 27 match 'Hello my name is coderasha.' As you see match is found but its printed like object. group(1) capture the text matched by the regex inside them into a numbered group that can be reused with a numbered backreference re

# We need to extarct the string
data = name = re.search( , data).group( )
print(name)

# Output: coderasha import "coderasha" from "Hello my name is coderasha." 'Hello my name is (.*).' 1 So, I am applying the same logic to find the date inside strings that crawled. The next step is, to find a table and continue to crawl other fields. Luckily, the table locates between center tags. However, we have to find data again using RegEx because table elements have no any special attribute. information = bs.find( ).get_text(strip= , separator= ) operating = re.search( , information).group( ).strip()
    legal_name = re.search( , information).group( ).strip()
    physical_address = re.search( , information).group( ).strip()
    mailing_address = re.search( , information).group( ).strip()
    usdot_address = re.search( , information).group( ).strip()
    power_units = re.search( , information).group( ).strip()
    drivers = re.search( , information).group( ).strip() # Get all texts inside table 'center' True '  ' # Find fields using RegEx 'Operating Status:(.*)Out' 1 'Legal Name:(.*)DBA' 1 'Physical Address:(.*)Phone' 1 'Mailing Address:(.*)USDOT' 1 'USDOT Number:(.*)State Carrier ID Number' 1 'Power Units:(.*)Drivers' 1 'Drivers:(.*)MCS-150 Form Date' 1 Step 4: Write data in CSV Once data crawled, it is time to create new csv file and write data into it. I prefer to create another function which will handle this action. csv open(usdot_address + , mode= , newline= , encoding= ) csv_file:
            fieldnames = [ , , , , , , ]
            writer = csv.DictWriter(csv_file, fieldnames=fieldnames)
            writer.writeheader()
            writer.writerow({ :date, : operating, : legal_name, :physical_address, : mailing_address, :power_units, :drivers }) import : def write_csv (date, operating, legal_name, physical_address, mailing_address, usdot_address, power_units, drivers) with '.csv' 'w' '' "utf-8" as 'Date' 'Operating Status' 'Legal_Name' 'Physical Address' 'Mailing Address' 'Power Units' 'Drivers' 'Date' 'Operating Status' 'Legal_Name' 'Physical Address' 'Mailing Address' 'Power Units' 'Drivers' CSV name must be unique, so I named it with or with other name ID of report page from crawled data usdot_address Step 5: Read excel file to crawl data for each dot The final step is to read excel file and pass these dots end of the URL to access the pages. We can use to read excel file xlrd xlrd

dots = [] loc = ( ) 
    wb = xlrd.open_workbook(loc) 
    sheet = wb.sheet_by_index( ) 
    sheet.cell_value( , ) i range( , ): dot = str(sheet.cell_value(i, )).replace( , )
        dots.append(dot) import : def read_excel_file () "dots.xls" 0 0 0 # First five dot in excel for in 1 5 # Convert floats to string and clean from .0 0 '.0' '' xlrd reads numbers as float so the best solution is to convert them to strings and use method to remove .0 end of the string and pass these dots into url: replace() dot dots:
    crawl_data( + dot) time.sleep( ) for in 'https://safer.fmcsa.dot.gov/query.asp?searchtype=ANY&query_type=queryCarrierSnapshot&query_param=USDOT&query_string=' # Sleep 5 seconds to avoid any errors 5 Here is the Full Code: re csv urllib.request urllib.request urlopen, Request bs4 BeautifulSoup xlrd time

dots = [] loc = ( ) 
    wb = xlrd.open_workbook(loc) 
    sheet = wb.sheet_by_index( ) 
    sheet.cell_value( , ) i range( , ): 
        dot = str(sheet.cell_value(i, )).replace( , )
        dots.append(dot) req = Request(url, headers={ : })
    html = urlopen(req).read()
    bs = BeautifulSoup(html, )
    bold_texts = bs.find_all( ) b bold_texts: :
            date = re.search( , b.get_text(strip= , separator= )).group( ).strip() len(date) > :
                date = date.split( , )[ ]
            print(date) AttributeError: information = bs.find( ).get_text(strip= , separator= )

    operating = re.search( , information).group( ).strip()
    legal_name = re.search( , information).group( ).strip()
    physical_address = re.search( , information).group( ).strip()
    mailing_address = re.search( , information).group( ).strip()
    usdot_address = re.search( , information).group( ).strip()
    power_units = re.search( , information).group( ).strip()
    drivers = re.search( , information).group( ).strip()

    write_csv(date, operating, legal_name, physical_address, mailing_address, usdot_address, power_units, drivers) open(usdot_address + , mode= , newline= , encoding= ) csv_file:
            fieldnames = [ , , , , , , ]
            writer = csv.DictWriter(csv_file, fieldnames=fieldnames)
            writer.writeheader()
            writer.writerow({ :date, : operating, : legal_name, :physical_address, : mailing_address, :power_units, :drivers })                


read_excel_file()
print(dots) dot dots:
    crawl_data( + dot)
    time.sleep( ) import import import from import from import import import : def read_excel_file () "dots.xls" 0 0 0 for in 1 5 0 '.0' '' : def crawl_data (url) 'User-Agent' 'Mozilla/5.0' 'html.parser' 'b' for in try 'The information below reflects the content of the FMCSA management information systems as of(.*).' True '  ' 1 if 11 "." 1 0 except pass 'center' True '  ' 'Operating Status:(.*)Out' 1 'Legal Name:(.*)DBA' 1 'Physical Address:(.*)Phone' 1 'Mailing Address:(.*)USDOT' 1 'USDOT Number:(.*)State Carrier ID Number' 1 'Power Units:(.*)Drivers' 1 'Drivers:(.*)MCS-150 Form Date' 1 : def write_csv (date, operating, legal_name, physical_address, mailing_address, usdot_address, power_units, drivers) with '.csv' 'w' '' "utf-8" as 'Date' 'Operating Status' 'Legal_Name' 'Physical Address' 'Mailing Address' 'Power Units' 'Drivers' 'Date' 'Operating Status' 'Legal_Name' 'Physical Address' 'Mailing Address' 'Power Units' 'Drivers' for in 'https://safer.fmcsa.dot.gov/query.asp?searchtype=ANY&query_type=queryCarrierSnapshot&query_param=USDOT&query_string=' 5 Mission Accomplished! I hope you liked the first post of new Upwork series. The job offer still open so if want you can send proposal to client using this code. I post similar stories at . Check it out. Reverse Python Video Tutorial available in . YouTube Channel - Reverse Python Stay Connected!

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