Data Science is rapidly becoming a vital discipline for all types of businesses. An ability to extract insight and meaning from a large pile of data is a skill set worth its weight in gold. Due to its versatility and ease of use, Python has become the programming language of choice for data scientists.
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In this Python cheat sheet, we will walk you through a couple of examples using two of the most used data types: the list and the Pandas DataFrame. The list is self-explanatory; it’s a collection of values set in a one-dimensional array. A Pandas DataFrame is just like a tabular spreadsheet, it has data laid out in columns and rows.
Let’s take a look at a few neat things we can do with lists and DataFrames in Python!
Get the pdf here.
Get the pdf here.
Python Cheat Sheet
- This is probably the easiest way to immediately apply calculus functions with Python. Referenced Websites. These are the the websites which I have heavily referenced to make this revision cheat sheet. I would recommend visiting these sites, especially Taking Derivatives In Python which goes through the same rules. Taking Derivatives In Python.
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- A detailed Python cheat sheet with key data types, functions, and commands you should learn as a beginner. Free to download as PDF and PNG. Free resource for helping beginners to build, manage and grow their websites.
Lists
Creating Lists
![Python Python](/uploads/1/3/7/3/137353452/706799694.png)
Create an empty list and use a for loop to append new values.
#add two to each value
my_list = []
for x in range(1,11):
my_list.append(x+2)
my_list = []
for x in range(1,11):
my_list.append(x+2)
We can also do this in one step using list comprehensions:
my_list = [x + 2 for x in range(1,11)]
Creating Lists with Conditionals
As above, we will create a list, but now we will only add 2 to the value if it is even.
#add two, but only if x is even
my_list = []
for x in range(1,11):
if x % 2 0:
my_list.append(x+2)
else:
my_list.append(x)
my_list = []
for x in range(1,11):
if x % 2 0:
my_list.append(x+2)
else:
my_list.append(x)
Using a list comp:
my_list = [x+2 if x % 2 0 else x
for x in range(1,11)]
for x in range(1,11)]
Selecting Elements and Basic Stats
Select elements by index.
#get the first/last element
first_ele = my_list[0]
last_ele = my_list[-1]
first_ele = my_list[0]
last_ele = my_list[-1]
Some basic stats on lists:
#get max/min/mean value
biggest_val = max(my_list)
smallest_val = min(my_list)avg_val = sum(my_list) / len(my_list)
biggest_val = max(my_list)
smallest_val = min(my_list)avg_val = sum(my_list) / len(my_list)
DataFrames
Reading in Data to a DataFrame
We first need to import the pandas module.
import pandas as pd
Then we can read in data from csv or xlsx files:
df_from_csv = pd.read_csv(‘path/to/my_file.csv’,
sep=’,’,
nrows=10)
xlsx = pd.ExcelFile(‘path/to/excel_file.xlsx’)
df_from_xlsx = pd.read_excel(xlsx, ‘Sheet1’)
sep=’,’,
nrows=10)
xlsx = pd.ExcelFile(‘path/to/excel_file.xlsx’)
df_from_xlsx = pd.read_excel(xlsx, ‘Sheet1’)
Slicing DataFrames
We can slice our DataFrame using conditionals.
df_filter = df[df[‘population’] > 1000000]
df_france = df[df[‘country’] ‘France’]
df_france = df[df[‘country’] ‘France’]
Sorting values by a column:
df.sort_values(by=’population’,
ascending=False)
ascending=False)
Filling Missing Values
Let’s fill in any missing values with that column’s average value.
df[‘population’] = df[‘population’].fillna(
value=df[‘population’].mean()
)
value=df[‘population’].mean()
)
Applying Functions to Columns
Apply a custom function to every value in one of the DataFrame’s columns.
def fix_zipcode(x):
”’
make sure that zipcodes all have leading zeros
”’
return str(x).zfill(5)
df[‘clean_zip’] = df[‘zip code’].apply(fix_zipcode)
”’
make sure that zipcodes all have leading zeros
”’
return str(x).zfill(5)
df[‘clean_zip’] = df[‘zip code’].apply(fix_zipcode)
Once you’ve put together enough web scrapers, you start to feel like you can do it in your sleep. I’ve probably built hundreds of scrapers over the years for my own projects, as well as for clients and students in my web scraping course.
Occasionally though, I find myself referencing documentation or re-reading old code looking for snippets I can reuse. One of the students in my course suggested I put together a “cheat sheet” of commonly used code snippets and patterns for easy reference.
I decided to publish it publicly as well – as an organized set of easy-to-reference notes – in case they’re helpful to others.
While it’s written primarily for people who are new to programming, I also hope that it’ll be helpful to those who already have a background in software or python, but who are looking to learn some web scraping fundamentals and concepts.
Table of Contents:
- Extracting Content from HTML
- Storing Your Data
- More Advanced Topics
Useful Libraries
For the most part, a scraping program deals with making HTTP requests and parsing HTML responses.
I always make sure I have
requests
and BeautifulSoup
installed before I begin a new scraping project. From the command line:Then, at the top of your
.py
file, make sure you’ve imported these libraries correctly.Making Simple Requests
Make a simple GET request (just fetching a page)
Make a POST requests (usually used when sending information to the server like submitting a form)
Pass query arguments aka URL parameters (usually used when making a search query or paging through results)
Inspecting the Response
See what response code the server sent back (useful for detecting 4XX or 5XX errors)
Access the full response as text (get the HTML of the page in a big string)
Look for a specific substring of text within the response
![Python 3 beginners cheat sheet pdf Python 3 beginners cheat sheet pdf](/uploads/1/3/7/3/137353452/450364308.png)
Check the response’s Content Type (see if you got back HTML, JSON, XML, etc)
Extracting Content from HTML
Now that you’ve made your HTTP request and gotten some HTML content, it’s time to parse it so that you can extract the values you’re looking for.
Using Regular Expressions
Using Regular Expressions to look for HTML patterns is famously NOT recommended at all.
However, regular expressions are still useful for finding specific string patterns like prices, email addresses or phone numbers.
Run a regular expression on the response text to look for specific string patterns:
Using BeautifulSoup
BeautifulSoup is widely used due to its simple API and its powerful extraction capabilities. It has many different parser options that allow it to understand even the most poorly written HTML pages – and the default one works great.
Compared to libraries that offer similar functionality, it’s a pleasure to use. To get started, you’ll have to turn the HTML text that you got in the response into a nested, DOM-like structure that you can traverse and search
Look for all anchor tags on the page (useful if you’re building a crawler and need to find the next pages to visit)
Look for all tags with a specific class attribute (eg
<li>...</li>
)Look for the tag with a specific ID attribute (eg:
<div>...</div>
)Look for nested patterns of tags (useful for finding generic elements, but only within a specific section of the page)
Look for all tags matching CSS selectors (similar query to the last one, but might be easier to write for someone who knows CSS)
Get a list of strings representing the inner contents of a tag (this includes both the text nodes as well as the text representation of any other nested HTML tags within)
Return only the text contents within this tag, but ignore the text representation of other HTML tags (useful for stripping our pesky
<span>
, <strong>
, <i>
, or other inline tags that might show up sometimes)Convert the text that are extracting from unicode to ascii if you’re having issues printing it to the console or writing it to files
Get the attribute of a tag (useful for grabbing the
src
attribute of an <img>
tag or the href
attribute of an <a>
tag)Putting several of these concepts together, here’s a common idiom: iterating over a bunch of container tags and pull out content from each of them
Using XPath Selectors
BeautifulSoup doesn’t currently support XPath selectors, and I’ve found them to be really terse and more of a pain than they’re worth. I haven’t found a pattern I couldn’t parse using the above methods.
If you’re really dedicated to using them for some reason, you can use the lxml library instead of BeautifulSoup, as described here.
Storing Your Data
Now that you’ve extracted your data from the page, it’s time to save it somewhere.
Note: The implication in these examples is that the scraper went out and collected all of the items, and then waited until the very end to iterate over all of them and write them to a spreadsheet or database.
I did this to simplify the code examples. In practice, you’d want to store the values you extract from each page as you go, so that you don’t lose all of your progress if you hit an exception towards the end of your scrape and have to go back and re-scrape every page.
Writing to a CSV
Probably the most basic thing you can do is write your extracted items to a CSV file. By default, each row that is passed to the
csv.writer
object to be written has to be a python list
.In order for the spreadsheet to make sense and have consistent columns, you need to make sure all of the items that you’ve extracted have their properties in the same order. This isn’t usually a problem if the lists are created consistently.
If you’re extracting lots of properties about each item, sometimes it’s more useful to store the item as a python
dict
instead of having to remember the order of columns within a row. The csv
module has a handy DictWriter
that keeps track of which column is for writing which dict key.Writing to a SQLite Database
You can also use a simple SQL insert if you’d prefer to store your data in a database for later querying and retrieval.
More Advanced Topics
These aren’t really things you’ll need if you’re building a simple, small scale scraper for 90% of websites. But they’re useful tricks to keep up your sleeve.
Javascript Heavy Websites
Contrary to popular belief, you do not need any special tools to scrape websites that load their content via Javascript. In order for the information to get from their server and show up on a page in your browser, that information had to have been returned in an HTTP response somewhere.
It usually means that you won’t be making an HTTP request to the page’s URL that you see at the top of your browser window, but instead you’ll need to find the URL of the AJAX request that’s going on in the background to fetch the data from the server and load it into the page.
There’s not really an easy code snippet I can show here, but if you open the Chrome or Firefox Developer Tools, you can load the page, go to the “Network” tab and then look through the all of the requests that are being sent in the background to find the one that’s returning the data you’re looking for. Start by filtering the requests to only
XHR
or JS
to make this easier.Once you find the AJAX request that returns the data you’re hoping to scrape, then you can make your scraper send requests to this URL, instead of to the parent page’s URL. If you’re lucky, the response will be encoded with
JSON
which is even easier to parse than HTML.Content Inside Iframes
This is another topic that causes a lot of hand wringing for no reason. Sometimes the page you’re trying to scrape doesn’t actually contain the data in its HTML, but instead it loads the data inside an iframe.
Again, it’s just a matter of making the request to the right URL to get the data back that you want. Make a request to the outer page, find the iframe, and then make another HTTP request to the iframe’s
src
attribute.Sessions and Cookies
While HTTP is stateless, sometimes you want to use cookies to identify yourself consistently across requests to the site you’re scraping.
The most common example of this is needing to login to a site in order to access protected pages. Without the correct cookies sent, a request to the URL will likely be redirected to a login form or presented with an error response.
However, once you successfully login, a session cookie is set that identifies who you are to the website. As long as future requests send this cookie along, the site knows who you are and what you have access to.
Delays and Backing Off
If you want to be polite and not overwhelm the target site you’re scraping, you can introduce an intentional delay or lag in your scraper to slow it down
Some also recommend adding a backoff that’s proportional to how long the site took to respond to your request. That way if the site gets overwhelmed and starts to slow down, your code will automatically back off.
Spoofing the User Agent
By default, the
requests
library sets the User-Agent
header on each request to something like “python-requests/2.12.4”. You might want to change it to identify your web scraper, perhaps providing a contact email address so that an admin from the target website can reach out if they see you in their logs.More commonly, this is used to make it appear that the request is coming from a normal web browser, and not a web scraping program.
Using Proxy Servers
Even if you spoof your User Agent, the site you are scraping can still see your IP address, since they have to know where to send the response.
If you’d like to obfuscate where the request is coming from, you can use a proxy server in between you and the target site. The scraped site will see the request coming from that server instead of your actual scraping machine.
If you’d like to make your requests appear to be spread out across many IP addresses, then you’ll need access to many different proxy servers. You can keep track of them in a
list
and then have your scraping program simply go down the list, picking off the next one for each new request, so that the proxy servers get even rotation.Beginners Python Cheat Sheet Examples
Setting Timeouts
If you’re experiencing slow connections and would prefer that your scraper moved on to something else, you can specify a timeout on your requests.
Beginners Python Cheat Sheet Pdf
Handling Network Errors
Just as you should never trust user input in web applications, you shouldn’t trust the network to behave well on large web scraping projects. Eventually you’ll hit closed connections, SSL errors or other intermittent failures.
Learn More
If you’d like to learn more about web scraping, I currently have an ebook and online course that I offer, as well as a free sandbox website that’s designed to be easy for beginners to scrape.
You can also subscribe to my blog to get emailed when I release new articles.