How to Build a Web Scraper With Python [Step-by-Step Guide]

On my self-taught programming journey, my interests lie within machine learning (ML) and artificial intelligence (AI), and the language I’ve chosen to master is Python.

My skills in Python are basic, so if you’re here with not a lot of skills in coding, I hope this guide helps you gain more knowledge and understanding.

The Perfect Beginner Project

To source data for ML, AI, or data science projects, you’ll often rely on databases, APIs, or ready-made CSV datasets. But what if you can’t find a dataset you want to use and analyze? That’s where a web scraper comes in.

Working on projects is crucial to solidifying the knowledge you gain. When I began this project, I was a little overwhelmed because I truly didn’t know a thing.

Sticking with it, finding answers to my questions on Stack Overflow, and a lot of trial and error helped me really understand how programming works — how web pages work, how to use loops, and how to build functions and keep data clean. It makes building a web scraper the perfect beginner project for anyone starting out in Python.

What we’ll cover

This guide will take you through understanding HTML web pages, building a web scraper using Python, and creating a DataFrame with pandas. It’ll cover data quality, data cleaning, and data-type conversion — entirely step by step and with instructions, code, and explanations on how every piece of it works. I hope you code along and enjoy!

Disclaimer

Websites can restrict or ban scraping data from their website. Users can be subject to legal ramifications depending on where and how you attempt to scrape information. Websites usually describe this in their terms of use and in their robots.txt file found at their site, which usually looks something like this: www.example.com/robots.txt. So scrape responsibly, and respect the robots.txt.

What’s Web Scraping?

Web scraping consists of gathering data available on websites. This can be done manually by a human or by using a bot.

A bot is a program you build that helps you extract the data you need much quicker than a human’s hand and eyes can.

What Are We Going to Scrape?

It’s essential to identify the goal of your scraping right from the start. We don’t want to scrape any data we don’t actually need.

For this project, we’ll scrape data from IMDb’s “Top 1,000” movies, specifically the top 50 movies on this page. Here is the information we’ll gather from each movie listing:

• The title
• The year it was released
• How long the movie is
• IMDb’s rating of the movie
• The Metascore of the movie
• How many votes the movie got
• The U.S. gross earnings of the movie

How Do Web Scrapers Work?

Web scrapers gather website data in the same way a human would: They go to a web page of the website, get the relevant data, and move on to the next web page — only much faster.

Every website has a different structure. These are a few important things to think about when building a web scraper:

• What’s the structure of the web page that contains the data you’re looking for?
• How do we get to those web pages?
• Will you need to gather more data from the next page?

The URL

To begin, let’s look at the URL of the page we want to scrape.

This is what we see in the URL:

We notice a few things about the URL:

1. ?

    acts as a separator — it indicates the end of the URL resource path and the start of the parameters
   

2. groups=top_1000

    specifies what the page will be about
   

3. &ref_adv_prv

    takes us to the the next or the previous page. The reference is the page we’re currently on. 

   adv_nxt

    and 

   adv_prv

    are two possible values — translated to advance to next page and advance to previous page.

When you navigate back and forth through the pages, you’ll notice only the parameters change. Keep this structure in mind as it’s helpful to know as we build the scraper.

The HTML

HTML stands for hypertext markup language, and most web pages are written using it. Essentially, HTML is how two computers speak to each other over the internet, and websites are what they say.

When you access an URL, your computer sends a request to the server that hosts the site. Any technology can be running on that server (JavaScript, Ruby, Java, etc.) to process your request. Eventually, the server returns a response to your browser; oftentimes, that response will be in the form of an HTML page for your browser to display.

HTML describes the structure of a web page semantically, and originally included cues for the appearance of the document.

Inspect HTML

Chrome, Firefox, and Safari users can examine the HTML structure of any page by right-clicking your mouse and pressing the Inspect option.

A menu will appear on the bottom or right-hand side of your page with a long list of all the HTML tags housing the information displayed to your browser window. If you’re in Safari (photo above), you’ll want to press the button to the left of the search bar, which looks like a target. If you’re in Chrome or Firefox, there’s a small box with an arrow icon in it at the top left that you’ll use to inspect.

Once clicked, if you move your cursor over any element of the page, you’ll notice it’ll get highlighted along with the HTML tags in the menu that they’re associated with, as seen above.

Knowing how to read the basic structure of a page’s HTML page is important so we can turn to Python to help us extract the HTML from the page.

Tools

The tools we’re going to use are:

• Repl (optional) is a simple, interactive computer-programming environment used via your web browser. I recommend using this just for code-along purposes if you don’t already have an IDE. If you use Repl, make sure you’re using the Python environment.
• Requests will allow us to send HTTP requests to get HTML files
• BeautifulSoup will help us parse the HTML files
• pandas will help us assemble the data into a DataFrame to clean and analyze it
• NumPy will add support for mathematical functions and tools for working with arrays

Now, Let’s Code

You can follow along below inside your Repl environment or IDE, or you can go directly to the entire code here. Have fun!

Import tools

First, we’ll import the tools we’ll need so we can use them to help us build the scraper and get the data we need.

Movies in English

It’s very likely when we run our code to scrape some of these movies, we’ll get the movie names translated into the main language of the country the movie originated in.

Use this code to make sure we get English-translated titles from all the movies we scrape:

Request contents of the URL

Get the contents of the page we’re looking at by requesting the URL:

Breaking URL requests down:

• url

   is the variable we create and assign the URL to

• results

   is the variable we create to store our request.get action

• requests.get(url, headers=headers)

   is the method we use to grab the contents of the URL.
• The 

  headers

   part tells our scraper to bring us English, based on our previous line of code.

Using BeautifulSoup

Make the content we grabbed easy to read by using BeautifulSoup:

Breaking BeautifulSoup down:

• soup

   is the variable we create to assign the method BeatifulSoup to, which specifies a desired format of results using the HTML parser — this allows Python to read the components of the page rather than treating it as one long string

• print(soup.prettify())

   will print what we’ve grabbed in a more structured tree format, making it easier to read

The results of the print will look more ordered, like this:

Initialize your storage

When we write code to extract our data, we need somewhere to store that data. Create variables for each type of data you’ll extract, and assign an empty list to it, indicated by square brackets 

[]

Your code should now look something like this. Note that we can delete our 

print

Find the right div container

It’s time to check out the HTML code in our web page.

Go to the web page we’re scraping, inspect it, and hover over a single movie in its entirety, like below:

We need to figure out what distinguishes each of these from other div containers we see.

You‘ll notice the list of 

div

class

lister-item

mode-advanced

If you click on each of those, you’ll notice it’ll highlight each movie container on the left of the page, like above.

If we do a quick search within inspect (press Ctrl+F and type 

lister-item mode-advanced

div

Find all 

lister-item mode-advanced

 divs

Our next move is to tell our scraper to find all of these lister-item mode-advanced divs:

Breaking 

find_all

• movie_div

   is the variable we’ll use to store all of the div containers with a class of 

  lister-item mode-advanced

• the 

  find_all()

   method extracts all the div containers that have a class attribute of lister-item mode-advanced from what we have stored in our variable soup.

Get ready to extract each item

If we look at the first movie on our list:

We’re missing gross earnings! If you look at the second movie, they’ve included it there.

Something to always consider when building a web scraper is the idea that not all the information you seek will be available for you to gather.

In these cases, we need to make sure our web scraper doesn’t stop working or break when it reaches missing data and build around the idea we just don’t know whether or not that’ll happen.

Getting into each 

lister-item mode-advanced

 div

When we grab each of the items we need in a single

lister-item mode-advanced

div

lister-item mode-advanced

div

Breaking down the for loop:

• A 

  for

   loop is used for iterating over a sequence. Our sequence being every 

  lister-item mode-advanced

  div

  container that we stored in 

  movie_div

• container

   is the name of the variable that enters each div. You can name this whatever you want (

  x

  , 

  loop

  , 

  banana

  , 

  cheese

  ), and it wont change the function of the loop.

It can be read like this:

Extract the title of the movie

Beginning with the movie’s name, let’s locate its corresponding HTML line by using inspect and clicking on the title.

We see the name is contained within an anchor tag, 

<a>

<h3>

<h3>

<div>

<div>

div

Breaking titles down:

• name

   is the variable we’ll use to store the title data we find

• container

   is what used in our

  for

  loop — it’s used for iterating over each time.

• h3

   and 

  .a

   is attribute notation and tells the scraper to access each of those tags.

• text

   tells the scraper to grab the text nested in the <a> tag

• titles.append(name)

   tells the scraper to take what we found and stored in name and to add it into our empty list called titles, which we created in the beginning

Extract year of release

Let’s locate the movie’s year and its corresponding HTML line by using inspect and clicking on the year.

We see this data is stored within the 

<span>

<a>

.h3.a

<a>

h3

<span>

<span>

Instead, we can tell our scraper to search by the distinctive mark of the second 

<span>

find()

find_all()

Breaking years down:

• year

   is the variable we’ll use to store the year data we find

• container

   is what we used in our for loop — it’s used for iterating over each time.

• h3

   is attribute notation, which tells the scraper to access that tag.

• find()

   is a method we’ll use to access this particular 

  <span>

   tag
• (

  ‘span’, class_ = ‘lister-item-year’

  ) is the distinctive 

  <span>

   tag we want

• years.append(year)

   tells the scraper to take what we found and stored in year and to add it into our empty list called years (which we created in the beginning)

Extract length of movie

Locate the movie’s length and its correspondent HTML line by using inspect and clicking on the total minutes.

The data we need can be found in a 

<span>

Breaking time down:

runtime

• container

   is what we used in our for loop — it’s used for iterating over each time.

• find()

   is a method we’ll use to access this particular 

  <span>

   tag
• (‘span’, class_ = ‘runtime’) is the distinctive 

  <span>

   tag we want

• if container.p.find(‘span’, class_=’runtime’) else ‘-’

   says if there’s data there, grab it — but if the data is missing, then put a dash there instead.

• text

   tells the scraper to grab that text in the 

  <span>

   tag

• time.append(runtime)

   tells the scraper to take what we found and stored in runtime and to add it into our empty list called time (which we created in the beginning)

Extract IMDb ratings

Find the movie’s IMDb rating and its corresponding HTML line by using inspect and clicking on the IMDb rating.

Now, we’ll focus on extracting the IMDb rating. The data we need can be found in a 

<strong>

<strong>

Breaking IMDb ratings down:

• imdb

   is the variable we’ll use to store the IMDB ratings data it finds

• container

   is what we used in our 

  for

   loop — it’s used for iterating over each time.

• strong

   is attribute notation that tells the scraper to access that tag.

• text

   tells the scraper to grab that text
• The 

  float()

   method turns the text we find into a float — which is a decimal

• imdb_ratings.append(imdb)

   tells the scraper to take what we found and stored in 

  imdb

   and to add it into our empty list called  

  imdb_ratings

   (which we created in the beginning).

Extract Metascore

Find the movie’s Metascore rating and its corresponding HTML line by using inspect and clicking on the Metascore number.

The data we need can be found in a 

<span>

metascore favorable

Before we settle on that, you should notice that, of course, a 96 for “Parasite” shows a favorable rating, but are the others favorable? If you highlight the next movie’s Metascore, you’ll see “JoJo Rabbit” has a class that says 

metascore mixed

metascore

Breaking Metascores down:

• m_score

   is the variable we’ll use to store the Metascore-rating data it finds

• container

   is what we used in our 

  for

   loop — it’s used for iterating over each time.

• find()

   is a method we’ll use to access this particular 

  <span>

   tag
• (

  ‘span’, class_ = ‘metascore’

  ) is the distinctive 

  <span>

   tag we want.

• text

   tells the scraper to grab that text

• if container.find(‘span’, class_=’metascore’) else ‘-’

   says if there is data there, grab it — but if the data is missing, then put a dash there
• The 

  int()

   method turns the text we find into an integer

• metascores.append(m_score)

   tells the scraper to take what we found and stored in 

  m_score

   and to add it into our empty list called  

  metascores

   (which we created in the beginning)

Extract votes and gross earnings

We’re finally onto the final two items we need to extract, but we saved the toughest for last.

Here’s where things get a little tricky. As mentioned earlier, you should have noticed that when we look at the first movie on this list, we don’t see a gross-earnings number. When we look at the second movie on the list, we can see both.

Let’s just have a look at the second movie’s HTML code and go from there.

Both the votes and the gross are highlighted on the right. After looking at the votes and gross containers for movie #2, what do you notice?

As you can see, both of these are in a 

<span>

name

nv

data-value

How can we grab the data for the second one if the search parameters for the first one are the same? How do we tell our scraper to skip over the first one and scrape the second?

This one took a lot of brain flexing, tons of coffee, and a couple late nights to figure out. Here’s how I did it:

Breaking votes and gross down:

• nv

   is an entirely new variable we’ll use to hold both the votes and the gross 

  <span>

   tags

• container

   is what we used in our 

  for

   loop for iterating over each time

• find_all()

   is the method we’ll use to grab both of the 

  <span>

   tags
• (

  ‘span’, attrs = ‘name’ : ’nv’

  ) is how we can grab attributes of that specific tag.

• vote

   is the variable we’ll use to store the votes we find in the 

  nv

   tag

• nv[0]

   tells the scraper to go into the 

  nv

   tag and grab the first data in the list — which are the votes because votes comes first in our HTML code (computers count in binary — they start count at 0, not 1).

• text

   tells the scraper to grab that text

• votes.append(vote)

   tells the scraper to take what we found and stored in 

  vote

   and to add it into our empty list called 

  votes

   (which we created in the beginning)

• grosses

   is the variable we’ll use to store the gross we find in the 

  nv

   tag

• nv[1]

   tells the scraper to go into the 

  nv

   tag and grab the second data in the list — which is gross because gross comes second in our HTML code

• nv[1].text if len(nv) > 1 else ‘-’

   says if the length of 

  nv

   is greater than one, then find the second datum that’s stored. But if the data that’s stored in 

  nv

   isn’t greater than one — meaning if the gross is missing — then put a dash there.

• us_gross.append(grosses)

   tells the scraper to take what we found and stored in 

  grosses

   and to add it into our empty list called  

  us_grosses

   (which we created in the beginning)

Your code should now look like this:

Let’s See What We Have So Far

Now that we’ve told our scraper what elements to scrape, let’s use the print function to print out each list we’ve sent our scraped data to:

Our lists looks like this

['Parasite', 'Jojo Rabbit', '1917', 'Knives Out', 'Uncut Gems', 'Once Upon a Time... in Hollywood', 'Joker', 'The Gentlemen', 'Ford v Ferrari', 'Little Women', 'The Irishman', 'The Lighthouse', 'Toy Story 4', 'Marriage Story', 'Avengers: Endgame', 'The Godfather', 'Blade Runner 2049', 'The Shawshank Redemption', 'The Dark Knight', 'Inglourious Basterds', 'Call Me by Your Name', 'The Two Popes', 'Pulp Fiction', 'Inception', 'Interstellar', 'Green Book', 'Blade Runner', 'The Wolf of Wall Street', 'Gone Girl', 'The Shining', 'The Matrix', 'Titanic', 'The Silence of the Lambs', 'Three Billboards Outside Ebbing, Missouri', "Harry Potter and the Sorcerer's Stone", 'The Peanut Butter Falcon', 'The Handmaiden', 'Memories of Murder', 'The Lord of the Rings: The Fellowship of the Ring', 'Gladiator', 'The Martian', 'Bohemian Rhapsody', 'Watchmen', 'Forrest Gump', 'Thor: Ragnarok', 'Casino Royale', 'The Breakfast Club', 'The Godfather: Part II', 'Django Unchained', 'Baby Driver']
['(2019)', '(2019)', '(2019)', '(2019)', '(2019)', '(2019)', '(2019)', '(2019)', '(2019)', '(2019)', '(2019)', '(I) (2019)', '(2019)', '(2019)', '(2019)', '(1972)', '(2017)', '(1994)', '(2008)', '(2009)', '(2017)', '(2019)', '(1994)', '(2010)', '(2014)', '(2018)', '(1982)', '(2013)', '(2014)', '(1980)', '(1999)', '(1997)', '(1991)', '(2017)', '(2001)', '(2019)', '(2016)', '(2003)', '(2001)', '(2000)', '(2015)', '(2018)', '(2009)', '(1994)', '(2017)', '(2006)', '(1985)', '(1974)', '(2012)', '(2017)']
['132 min', '108 min', '119 min', '131 min', '135 min', '161 min', '122 min', '113 min', '152 min', '135 min', '209 min', '109 min', '100 min', '137 min', '181 min', '175 min', '164 min', '142 min', '152 min', '153 min', '132 min', '125 min', '154 min', '148 min', '169 min', '130 min', '117min', '180 min', '149 min', '146 min', '136 min', '194 min', '118 min', '115 min', '152 min', '97 min', '145 min', '132 min', '178 min', '155 min', '144 min', '134 min', '162 min', '142 min', '130 min', '144 min', '97 min', '202 min', '165 min', '113 min']
[8.6, 8.0, 8.5, 8.0, 7.6, 7.7, 8.6, 8.1, 8.2, 8.0, 8.0, 7.7, 7.8, 8.0, 8.5, 9.2, 8.0, 9.3, 9.0, 8.3, 7.9, 7.6, 8.9, 8.8, 8.6, 8.2, 8.1, 8.2, 8.1,8.4, 8.7, 7.8, 8.6, 8.2, 7.6, 7.7, 8.1, 8.1, 8.8, 8.5, 8.0, 8.0, 7.6, 8.8, 7.9, 8.0, 7.9, 9.0, 8.4, 7.6]
['96        ', '58        ', '78        ', '82        ', '90        ', '83        ', '59        ', '51        ', '81        ', '91        ', '94      ', '83        ', '84        ', '93        ', '78        ', '100        ', '81        ', '80        ', '84        ', '69        ', '93 ', '75        ', '94        ', '74        ', '74        ', '69        ', '84        ', '75        ', '79        ', '66        ', '73        ', '75        ', '85        ', '88        ', '64        ', '70        ', '84        ', '82        ', '92        ', '67        ', '80        ', '49   ', '56        ', '82        ', '74        ', '80        ', '62        ', '90        ', '81        ', '86        ']
['282,699', '142,517', '199,638', '195,728', '108,330', '396,071', '695,224', '42,015', '152,661', '65,234', '249,950', '77,453', '160,180', '179,887', '673,115', '1,511,929', '414,992', '2,194,397', '2,176,865', '1,184,882', '178,688', '76,291', '1,724,518', '1,925,684', '1,378,968', '293,695', '656,442', '1,092,063', '799,696', '835,496', '1,580,250', '994,453', '1,191,182', '383,958', '595,613', '34,091', '92,492', '115,125', '1,572,354', '1,267,310', '715,623', '410,199', '479,811', '1,693,344', '535,065', '555,756', '330,308', '1,059,089', '1,271,569', '398,553']
['-', '$0.35M', '-', '-', '-', '$135.37M', '$192.73M', '-', '-', '-', '-', '$0.43M', '$433.03M', '-', '$858.37M', '$134.97M', '$92.05M', '$28.34M', '$534.86M', '$120.54M', '$18.10M', '-', '$107.93M', '$292.58M', '$188.02M', '$85.08M', '$32.87M', '$116.90M', '$167.77M', '$44.02M', '$171.48M', '$659.33M', '$130.74M', '$54.51M', '$317.58M', '$13.12M', '$2.01M', '$0.01M', '$315.54M', '$187.71M', '$228.43M', '$216.43M', '$107.51M', '$330.25M', '$315.06M', '$167.45M', '$45.88M', '$57.30M', '$162.81M', '$107.83M']

So far so good, but we aren’t quite there yet. We need to clean up our data a bit. Looks like we have some unwanted elements in our data: dollar signs,Ms, mins, commas, parentheses, and extra white space in the Metascores.

Building a DataFrame With pandas

The next order of business is to build a DataFrame with pandas to store the data we have nicely in a table to really understand what’s going on.

Here’s how we do it:

Breaking our dataframe down:

• movies

   is what we’ll name our DataFrame

• pd.DataFrame

   is how we initialize the creation of a DataFrame with pandas
• The keys on the left are the column names
• The values on the right are our lists of data we’ve scraped

View Our DataFrame

We can see how it all looks by simply using the print function on our DataFrame — which we called movies — at the bottom of our program:

Our pandas DataFrame looks like this

Data Quality

Before embarking on projects like this, you must know what your data-quality criteria is — meaning, what rules or constraints should your data follow. Here are some examples:

• Data-type constraints: Values in your columns must be a particular data type: numeric, boolean, date, etc.
• Mandatory constraints: Certain columns can’t be empty
• Regular expression patterns: Text fields that have to be in a certain pattern, like phone numbers

What Is Data Cleaning?

Data cleaning is the process of detecting and correcting or removing corrupt or inaccurate records from your dataset.

When doing data analysis, it’s also important to make sure we’re using the correct data types.

Checking Data Types

We can check what our data types look like by running this print function at the bottom of our program:

Our data type results

Lets analyze this: Our movie data type is an object, which is the same as a string, which would be correct considering they’re titles of movies. Our IMDb score is also correct because we have floating-point numbers in this column (decimal numbers).

But our 

year

timeMin

metascore

votes

us_grossMillions

float

Initially, when we were telling our scraper to grab these values from each HTML container, we were telling it to grab specific values from a string. A string represents text rather than numbers — it’s comprised of a set of characters that can also contain numbers.

For example, the word 

cheese

phrase I ate 10 blocks of cheese

10

I ate 10 blocks of cheese

10

Data Cleaning With pandas

Now that we have a clear idea of what our data looks like right now, it’s time to start cleaning it up.

This can be a tedious task, but it’s one that’s very important.

Cleaning year data

To remove the parentheses from our year data and to convert the object into an integer data type, we’ll do this:

Breaking cleaning year data down:

• movies[‘year’]

   tells pandas to go to the column year in our 

  DataFrame

• .str.extract(‘(\d+’)

   this method: 

  (‘(\d+’)

   says to extract all the digits in the string
• The 

  .astype(int)

   method converts the result to an integer

Now, if we run 

print(movies[‘year’])

You should see your list of years without any parentheses. And the data type showing is now an integer. Our year data is officially cleaned.

Cleaning time data

We’ll do exactly what we did cleaning our year data above to our time data by grabbing only the digits and converting our data type to an integer.

Cleaning Metascore data

The only cleaning we need to do here is converting our object data type into an integer:

Cleaning votes

With votes, we need to remove the commas and convert it into an integer data type:

Breaking cleaning votes down:

• movies[‘votes’]

   is our votes data in our movies 

  DataFrame

  . We’re assigning our new cleaned up data to our votes 

  DataFrame

  .

• .str.replace(‘ , ’ , ‘’)

   grabs the string and uses the 

  replace

   method to replace the commas with an empty quote (nothing)
• The 

  .astype(int)

   method converts the result into an integer

Cleaning gross data

The gross data involves a few hurdles to jump. What we need to do is remove the dollar sign and the Ms from the data and convert it into a floating-point number. Here’s how to do it:

Breaking cleaning gross down:

Top cleaning code:

• movies[‘us_grossMillions’]

   is our gross data in our movies 

  DataFrame

  . We’ll be assigning our new cleaned up data to our 

  us_grossMillions

   column.

• movies[‘us_grossMillions’]

   tells pandas to go to the

  column us_grossMillions

   in our 

  DataFrame

• The 

  .map()

   function calls the specified function for each item of an iterable

• lambda x: x

   is an anonymous functions in Python (one without a name). Normal functions are defined using the 

  def

   keyword.

• lstrip(‘$’).rstrip(‘M’)

   is our function arguments. This tells our function to strip the 

  $

   from the left side and strip the 

  M

   from the right side.

Bottom conversion code:

• movies[‘us_grossMillions’]

   is stripped of the elements we don’t need, and now we’ll assign the conversion code data to it to finish it up

• pd.to_numeric

   is a method we can use to change this column to a float. The reason we use this is because we have a lot of dashes in this column, and we can’t just convert it to a float using .astype(float) — this would catch an error.

• errors=’coerce’

   will transform the nonnumeric values, our dashes, into NaN (not-a-number ) values because we have dashes in place of the data that’s missing

Review the Cleaned and Converted Code

Let’s see how we did. Run the print function to see our data and the data types we have:

The result of our cleaned data

The result of our data types

Looks good!

Final Finished Code

Here’s the final code of your single page web scraper:

Saving Your Data to a CSV

What’s the use of our scraped data if we can’t save it for any future projects or analysis? Below is the code you can add to the bottom of your program to save your data to a CSV file:

Breaking the CSV file down:

• movies.to_csv('movies.csv')

In order for this code to run successfully, you’ll need to create an empty file and name it whatever you want — making sure it has the 

.csv

movies.csv

If you’re in Repl, you can create an empty CSVfile by hovering near Files and clicking the “Add file” option. Name it, and save it with a 

.csv

movies.to_csv(‘the_name_of_your_csv_here.csv’)

All your data should populate over into your CSV. Once you download it onto your computer/open it up, your file will look like this:

Conclusion

We’ve come a long way from requesting the HTML content of our web page to cleaning our entire DataFrame. You should now know how to scrape web pages with the same HTML and URL structure I’ve shown you above. Here’s a summary of what we’ve accomplished:

Next steps

I hope you had fun making this!

If you’d like to build on what you’ve learned, here are a few ideas to try out:

• Grab the movie data for all 1,000 movies on that list
• Scrape other data about each movie — e.g., genre, director, starring, or the summary of the movie
• Find a different website to scrape that interests you

In my next piece, I’ll explain how to loop through all of the pages of this IMDb list to grab all of the 1,000 movies, which will involve a few alterations to the final code we have here.

Happy coding!

Previously published at https://medium.com/better-programming/the-only-step-by-step-guide-youll-need-to-build-a-web-scraper-with-python-e79066bd895a