ProxyBoys

tags. So, I will find the div tags with those respective class-names, extract the data and store the data in a variable. Refer the code below:content = ge_source
soup = BeautifulSoup(content)
for a in ndAll(‘a’, href=True, attrs={‘class’:’_31qSD5′}):
(‘div’, attrs={‘class’:’_3wU53n’})
(‘div’, attrs={‘class’:’_1vC4OE _2rQ-NK’})
(‘div’, attrs={‘class’:’hGSR34 _2beYZw’})
()
Step 5: Run the code and extract the dataTo run the code, use the below command: python 6: Store the data in a required formatAfter extracting the data, you might want to store it in a format. This format varies depending on your requirement. For this example, we will store the extracted data in a CSV (Comma Separated Value) format. To do this, I will add the following lines to my code:df = Frame({‘Product Name’:products, ‘Price’:prices, ‘Rating’:ratings})
_csv(”, index=False, encoding=’utf-8′)Now, I’ll run the whole code again. A file name “” is created and this file contains the extracted data. I hope you guys enjoyed this article on “Web Scraping with Python”. I hope this blog was informative and has added value to your knowledge. Now go ahead and try Web Scraping. Experiment with different modules and applications of Python. If you wish to know about Web Scraping With Python on Windows platform, then the below video will help you understand how to do Scraping With Python | Python Tutorial | Web Scraping Tutorial | EdurekaThis Edureka live session on “WebScraping using Python” will help you understand the fundamentals of scraping along with a demo to scrape some details from a question regarding “web scraping with Python”? You can ask it on edureka! Forum and we will get back to you at the earliest or you can join our Python Training in Hobart get in-depth knowledge on Python Programming language along with its various applications, you can enroll here for live online Python training with 24/7 support and lifetime access.

Web Scraping using Python – DataCamp

Web scraping is a term used to describe the use of a program or algorithm to extract and process large amounts of data from the web. Whether you are a data scientist, engineer, or anybody who analyzes large amounts of datasets, the ability to scrape data from the web is a useful skill to have. Let’s say you find data from the web, and there is no direct way to download it, web scraping using Python is a skill you can use to extract the data into a useful form that can be imported.
In this tutorial, you will learn about the following:
• Data extraction from the web using Python’s Beautiful Soup module
• Data manipulation and cleaning using Python’s Pandas library
• Data visualization using Python’s Matplotlib library
The dataset used in this tutorial was taken from a 10K race that took place in Hillsboro, OR on June 2017. Specifically, you will analyze the performance of the 10K runners and answer questions such as:
• What was the average finish time for the runners?
• Did the runners’ finish times follow a normal distribution?
• Were there any performance differences between males and females of various age groups?
Using Jupyter Notebook, you should start by importing the necessary modules (pandas, numpy,, seaborn). If you don’t have Jupyter Notebook installed, I recommend installing it using the Anaconda Python distribution which is available on the internet. To easily display the plots, make sure to include the line%matplotlib inline as shown below.
import pandas as pd
import numpy as np
import as plt
import seaborn as sns%matplotlib inline
To perform web scraping, you should also import the libraries shown below. The quest module is used to open URLs. The Beautiful Soup package is used to extract data from html files. The Beautiful Soup library’s name is bs4 which stands for Beautiful Soup, version 4.
from quest import urlopen
from bs4 import BeautifulSoup
After importing necessary modules, you should specify the URL containing the dataset and pass it to urlopen() to get the html of the page.
url = ”
html = urlopen(url)
Getting the html of the page is just the first step. Next step is to create a Beautiful Soup object from the html. This is done by passing the html to the BeautifulSoup() function. The Beautiful Soup package is used to parse the html, that is, take the raw html text and break it into Python objects. The second argument ‘lxml’ is the html parser whose details you do not need to worry about at this point.
soup = BeautifulSoup(html, ‘lxml’)
type(soup)
autifulSoup
The soup object allows you to extract interesting information about the website you’re scraping such as getting the title of the page as shown below.
# Get the title
title =
print(title)

You can also get the text of the webpage and quickly print it out to check if it is what you expect.
# Print out the text
text = t_text()
#print()
You can view the html of the webpage by right-clicking anywhere on the webpage and selecting “Inspect. ” This is what the result looks like.
You can use the find_all() method of soup to extract useful html tags within a webpage. Examples of useful tags include < a > for hyperlinks, < table > for tables, < tr > for table rows, < th > for table headers, and < td > for table cells. The code below shows how to extract all the hyperlinks within the webpage.
nd_all(‘a’)
[5K,
Individual Results,
Team Results,
[email protected],
Results,
,
,
Huber Timing,
]
As you can see from the output above, html tags sometimes come with attributes such as class, src, etc. These attributes provide additional information about html elements. You can use a for loop and the get(‘”href”) method to extract and print out only hyperlinks.
all_links = nd_all(“a”)
for link in all_links:
print((“href”))
/results/2017GPTR
#individual
#team
mailto:[email protected]
#tabs-1
None
To print out table rows only, pass the ‘tr’ argument in nd_all().
# Print the first 10 rows for sanity check
rows = nd_all(‘tr’)
print(rows[:10])
[

Finishers: 577

,

Male: 414

,

Female: 163

,

Place Bib Name Gender City State Chip Time Chip Pace Gender Place Age Group Age Group Place Time to Start Gun Time Team

,

1 814 JARED WILSON M TIGARD OR 00:36:21 05:51 1 of 414 M 36-45 1 of 152 00:00:03 00:36:24 2 573 NATHAN A SUSTERSIC PORTLAND 00:36:42 05:55 2 of 414 M 26-35 1 of 154 00:36:45 INTEL TEAM F 3 687 FRANCISCO MAYA 00:37:44 06:05 3 of 414 M 46-55 1 of 64 00:00:04 00:37:48 4 623 PAUL MORROW BEAVERTON 00:38:34 06:13 4 of 414 2 of 152 00:38:37 5 569 DEREK G OSBORNE HILLSBORO 00:39:21 06:20 5 of 414 2 of 154 00:39:24 6 642 JONATHON TRAN 00:39:49 06:25 6 of 414 M 18-25 1 of 34 00:00:06 00:39:55

]
The goal of this tutorial is to take a table from a webpage and convert it into a dataframe for easier manipulation using Python. To get there, you should get all table rows in list form first and then convert that list into a dataframe. Below is a for loop that iterates through table rows and prints out the cells of the rows.
for row in rows:
row_td = nd_all(‘td’)
print(row_td)
type(row_td)
[

14TH

,

INTEL TEAM M

,

04:43:23

,

00:58:59 – DANIELLE CASILLAS

,

01:02:06 – RAMYA MERUVA

,

01:17:06 – PALLAVI J SHINDE

,

01:25:11 – NALINI MURARI

]
sultSet
The output above shows that each row is printed with html tags embedded in each row. This is not what you want. You can use remove the html tags using Beautiful Soup or regular expressions.
The easiest way to remove html tags is to use Beautiful Soup, and it takes just one line of code to do this. Pass the string of interest into BeautifulSoup() and use the get_text() method to extract the text without html tags.
str_cells = str(row_td)
cleantext = BeautifulSoup(str_cells, “lxml”). get_text()
print(cleantext)
[14TH, INTEL TEAM M, 04:43:23, 00:58:59 – DANIELLE CASILLAS, 01:02:06 – RAMYA MERUVA, 01:17:06 – PALLAVI J SHINDE, 01:25:11 – NALINI MURARI]
Using regular expressions is highly discouraged since it requires several lines of code and one can easily make mistakes. It requires importing the re (for regular expressions) module. The code below shows how to build a regular expression that finds all the characters inside the < td > html tags and replace them with an empty string for each table row.
First, you compile a regular expression by passing a string to match to mpile(). The dot, star, and question mark (. *? ) will match an opening angle bracket followed by anything and followed by a closing angle bracket. It matches text in a non-greedy fashion, that is, it matches the shortest possible string. If you omit the question mark, it will match all the text between the first opening angle bracket and the last closing angle bracket. After compiling a regular expression, you can use the () method to find all the substrings where the regular expression matches and replace them with an empty string. The full code below generates an empty list, extract text in between html tags for each row, and append it to the assigned list.
import re
list_rows = []
cells = nd_all(‘td’)
str_cells = str(cells)
clean = mpile(‘<. *? >‘)
clean2 = ((clean, ”, str_cells))
(clean2)
print(clean2)
type(clean2)
str
The next step is to convert the list into a dataframe and get a quick view of the first 10 rows using Pandas.
df = Frame(list_rows)
(10)
0
[Finishers:, 577]
1
[Male:, 414]
2
[Female:, 163]
3
[]
4
[1, 814, JARED WILSON, M, TIGARD, OR, 00:36:21…
5
[2, 573, NATHAN A SUSTERSIC, M, PORTLAND, OR,…
6
[3, 687, FRANCISCO MAYA, M, PORTLAND, OR, 00:3…
7
[4, 623, PAUL MORROW, M, BEAVERTON, OR, 00:38:…
8
[5, 569, DEREK G OSBORNE, M, HILLSBORO, OR, 00…
9
[6, 642, JONATHON TRAN, M, PORTLAND, OR, 00:39…
The dataframe is not in the format we want. To clean it up, you should split the “0” column into multiple columns at the comma position. This is accomplished by using the () method.
df1 = df[0](‘, ‘, expand=True)
This looks much better, but there is still work to do. The dataframe has unwanted square brackets surrounding each row. You can use the strip() method to remove the opening square bracket on column “0. ”
df1[0] = df1[0](‘[‘)
The table is missing table headers. You can use the find_all() method to get the table headers.
col_labels = nd_all(‘th’)
Similar to table rows, you can use Beautiful Soup to extract text in between html tags for table headers.
all_header = []
col_str = str(col_labels)
cleantext2 = BeautifulSoup(col_str, “lxml”). get_text()
(cleantext2)
print(all_header)
[‘[Place, Bib, Name, Gender, City, State, Chip Time, Chip Pace, Gender Place, Age Group, Age Group Place, Time to Start, Gun Time, Team]’]
You can then convert the list of headers into a pandas dataframe.
df2 = Frame(all_header)
()
[Place, Bib, Name, Gender, City, State, Chip T…
Similarly, you can split column “0” into multiple columns at the comma position for all rows.
df3 = df2[0](‘, ‘, expand=True)
The two dataframes can be concatenated into one using the concat() method as illustrated below.
frames = [df3, df1]
df4 = (frames)
Below shows how to assign the first row to be the table header.
df5 = ([0])
At this point, the table is almost properly formatted. For analysis, you can start by getting an overview of the data as shown below.

Int64Index: 597 entries, 0 to 595
Data columns (total 14 columns):
[Place 597 non-null object
Bib 596 non-null object
Name 593 non-null object
Gender 593 non-null object
City 593 non-null object
State 593 non-null object
Chip Time 593 non-null object
Chip Pace 578 non-null object
Gender Place 578 non-null object
Age Group 578 non-null object
Age Group Place 578 non-null object
Time to Start 578 non-null object
Gun Time 578 non-null object
Team] 578 non-null object
dtypes: object(14)
memory usage: 70. 0+ KB
(597, 14)
The table has 597 rows and 14 columns. You can drop all rows with any missing values.
df6 = (axis=0, how=’any’)
Also, notice how the table header is replicated as the first row in df5. It can be dropped using the following line of code.
df7 = ([0])
You can perform more data cleaning by renaming the ‘[Place’ and ‘ Team]’ columns. Python is very picky about space. Make sure you include space after the quotation mark in ‘ Team]’.
(columns={‘[Place’: ‘Place’}, inplace=True)
(columns={‘ Team]’: ‘Team’}, inplace=True)
The final data cleaning step involves removing the closing bracket for cells in the “Team” column.
df7[‘Team’] = df7[‘Team’](‘]’)
It took a while to get here, but at this point, the dataframe is in the desired format. Now you can move on to the exciting part and start plotting the data and computing interesting statistics.
The first question to answer is, what was the average finish time (in minutes) for the runners? You need to convert the column “Chip Time” into just minutes. One way to do this is to convert the column to a list first for manipulation.
time_list = df7[‘ Chip Time’]()
# You can use a for loop to convert ‘Chip Time’ to minutes
time_mins = []
for i in time_list:
h, m, s = (‘:’)
math = (int(h) * 3600 + int(m) * 60 + int(s))/60
(math)
#print(time_mins)
The next step is to convert the list back into a dataframe and make a new column (“Runner_mins”) for runner chip times expressed in just minutes.
df7[‘Runner_mins’] = time_mins
The code below shows how to calculate statistics for numeric columns only in the dataframe.
scribe(include=[])
Runner_mins
count
577. 000000
mean
60. 035933
std
11. 970623
min
36. 350000
25%
51. 000000
50%
59. 016667
75%
67. 266667
max
101. 300000
Interestingly, the average chip time for all runners was ~60 mins. The fastest 10K runner finished in 36. 35 mins, and the slowest runner finished in 101. 30 minutes.
A boxplot is another useful tool to visualize summary statistics (maximum, minimum, medium, first quartile, third quartile, including outliers). Below are data summary statistics for the runners shown in a boxplot. For data visualization, it is convenient to first import parameters from the pylab module that comes with matplotlib and set the same size for all figures to avoid doing it for each figure.
from pylab import rcParams
rcParams[‘gsize’] = 15, 5
xplot(column=’Runner_mins’)
(True, axis=’y’)
(‘Chip Time’)
([1], [‘Runners’])
([< at 0x570dd106d8>],
)
The second question to answer is: Did the runners’ finish times follow a normal distribution?
Below is a distribution plot of runners’ chip times plotted using the seaborn library. The distribution looks almost normal.
x = df7[‘Runner_mins’]
ax = sns. distplot(x, hist=True, kde=True, rug=False, color=’m’, bins=25, hist_kws={‘edgecolor’:’black’})
The third question deals with whether there were any performance differences between males and females of various age groups. Below is a distribution plot of chip times for males and females.
f_fuko = [df7[‘ Gender’]==’ F’][‘Runner_mins’]
m_fuko = [df7[‘ Gender’]==’ M’][‘Runner_mins’]
sns. distplot(f_fuko, hist=True, kde=True, rug=False, hist_kws={‘edgecolor’:’black’}, label=’Female’)
sns. distplot(m_fuko, hist=False, kde=True, rug=False, hist_kws={‘edgecolor’:’black’}, label=’Male’)
< at 0x570e301fd0>
The distribution indicates that females were slower than males on average. You can use the groupby() method to compute summary statistics for males and females separately as shown below.
g_stats = oupby(” Gender”, as_index=True). describe()
print(g_stats)
Runner_mins \
count mean std min 25% 50%
Gender
F 163. 0 66. 119223 12. 184440 43. 766667 58. 758333 64. 616667
M 414. 0 57. 640821 11. 011857 36. 350000 49. 395833 55. 791667
75% max
F 72. 058333 101. 300000
M 64. 804167 98. 516667
The average chip time for all females and males was ~66 mins and ~58 mins, respectively. Below is a side-by-side boxplot comparison of male and female finish times.
xplot(column=’Runner_mins’, by=’ Gender’)
ptitle(“”)
C:\Users\smasango\AppData\Local\Continuum\anaconda3\lib\site-packages\numpy\core\ FutureWarning: reshape is deprecated and will raise in a subsequent release. Please use (… ) instead
return getattr(obj, method)(*args, **kwds)
Text(0. 5, 0. 98, ”)
In this tutorial, you performed web scraping using Python. You used the Beautiful Soup library to parse html data and convert it into a form that can be used for analysis. You performed cleaning of the data in Python and created useful plots (box plots, bar plots, and distribution plots) to reveal interesting trends using Python’s matplotlib and seaborn libraries. After this tutorial, you should be able to use Python to easily scrape data from the web, apply cleaning techniques and extract useful insights from the data.
If you would like to learn more about Python, take DataCamp’s free Intro to Python for Data Science course.

How to Crawl a Website with DeepCrawl

Running frequent and targeted crawls of your website is a key part of improving it’s technical health and improving rankings in organic search. In this guide, you’ll learn how to a crawl a website efficiently and effectively with DeepCrawl. The six steps to crawling a website include:
Configuring the URL sources
Understanding the domain structure
Running a test crawl
Adding crawl restrictions
Testing your changes
Running your crawl
Step 1: Configuring the URL sources
There are six types of URL sources you can include in your DeepCrawl projects.
Including each one strategically, is the key to an efficient, and comprehensive crawl:
Web crawl: Crawl only the site by following its links to deeper levels.
Sitemaps: Crawl a set of sitemaps, and the URLs in those sitemaps. Links on these pages will not be followed or crawled.
Analytics: Upload analytics source data, and crawl the URLs, to discover additional landing pages on your site which may not be linked. The analytics data will be available in various reports.
Backlinks: Upload backlink source data, and crawl the URLs, to discover additional URLs with backlinks on your site. The backlink data will be available in various reports.
URL lists: Crawl a fixed list of URLs. Links on these pages will not be followed or crawled.
Log files: Upload log file summary data from log file analyser tools, such as Splunk and
Ideally, a website should be crawled in full (including every linked URL on the site). However, very large websites, or sites with many architectural problems, may not be able to be fully crawled immediately. It may be necessary to restrict the crawl to certain sections of the site, or limit specific URL patterns (we’ll cover how to do this below).
Step 2: Understanding the Domain Structure
Before starting a crawl, it’s a good idea to get a better understanding of your site’s domain structure:
Check the www/non-www and / configuration of the domain when you add the domain.
Identify whether the site is using sub-domains.
If you are not sure about sub-domains, check the DeepCrawl “Crawl Subdomains” option and they will automatically be discovered if they are linked.
Step 3: Running a Test Crawl
Start with a small “Web Crawl, ” to look for signs that the site is uncrawlable.
Before starting the crawl, ensure that you have set the “Crawl Limit” to a low quantity. This will make your first checks more efficient, as you won’t have to wait very long to see the results.
Problems to watch for include:
A high number of URLs returning error codes, such as 401 access denied
URLs returned that are not of the correct subdomain – check that the base domain is correct under “Project Settings”.
Very low number of URLs found.
A large number of failed URLs (502, 504, etc).
A large number of canonicalized URLs.
A large number of duplicate pages.
A significant increase in the number of pages found at each level.
To save time, and check for obvious problems immediately, download the URLs during the crawl:
Step 4: Adding Crawl Restrictions
Next, reduce the size of the crawl by identifying anything that can be excluded. Adding restrictions ensures you are not wasting time (or credits) crawling URLs that are not important to you. All the following restrictions can be added within the “Advanced Settings” tab.
Remove Parameters
If you have excluded any parameters from search engine crawls with URL parameter tools like Google Search Console, enter these in the “Remove Parameters” field under “Advanced Settings. ”
Add Custom Settings
DeepCrawl’s “Robots Overwrite” feature allows you to identify additional URLs that can be excluded using a custom file – allowing you to test the impact of pushing a new file to a live environment.
Upload the alternative version of your robots file under “Advanced Settings” and select “Use Robots Override” when starting the crawl:
Filter URLs and URL Paths
Use the “Included/Excluded” URL fields under “Advanced Settings” to limit the crawl to specific areas of interest.
Add Crawl Limits for Groups of Pages
Use the “Page Grouping” feature, under “Advanced Settings, ” to restrict the number of URLs crawled for groups of pages based on their URL patterns.
Here, you can add a name.
In the “Page URL Match” column you can add a regular expression.
Add a maximum number of URLs to crawl in the “Crawl Limit” column.
URLs matching the designated path are counted. When the limits have been reached, all further matching URLs go into the “Page Group Restrictions” report and are not crawled.
Step 5: Testing Your Changes
Run test “Web Crawls” to ensure your configuration is correct and you’re ready to run a full crawl.
Step 6: Running your Crawl
Ensure you’ve increased the “Crawl Limit” before running a more in-depth crawl.
Consider running a crawl with as many URL sources as possible, to supplement your linked URLs with XML Sitemap and Google Analytics, and other data.
If you have specified a subdomain of www within the “Base Domain” setting, subdomains such as blog or default, will not be crawled.
To include subdomains select “Crawl Subdomains” within the “Project Settings” tab.
Set “Scheduling” for your crawls and track your progress.
Handy Tips
Settings for Specific Requirements
If you have a test/sandbox site you can run a “Comparison Crawl” by adding your test site domain and authentication details in “Advanced Settings. ”
For more about the Test vs Live feature, check out our guide to Comparing a Test Website to a Live Website.
To crawl an AJAX-style website, with an escaped fragment solution, use the “URL Rewrite” function to modify all linked URLs to the escaped fragment format.
Read more about our testing features – Testing Development Changes Before Putting Them Live.
Changing Crawl Rate
Watch for performance issues caused by the crawler while running a crawl.
If you see connection errors, or multiple 502/503 type errors, you may need to reduce the crawl rate under “Advanced Settings. ”
If you have a robust hosting solution, you may be able to crawl the site at a faster rate.
The crawl rate can be increased at times when the site load is reduced – 4 a. m. for example.
Head to “Advanced Settings” > “Crawl Rate” > “Add Rate Restriction. ”
Analyze Outbound Links
Sites with a large quantity of external links, may want to ensure that users are not directed to dead links.
To check this, select “Crawl External Links” under “Project Settings, ” adding an HTTP status code next to external links within your report.
Read more on outbound link audits to learn about analyzing and cleaning up external links.
Change User Agent
See your site through a variety of crawlers’ eyes (Facebook/Bingbot etc. ) by changing the user agent in “Advanced Settings. ”
Add a custom user agent to determine how your website responds.
After The Crawl
Reset your “Project Settings” after the crawl, so you can continue to crawl with ‘real-world’ settings applied.
Remember, the more you experiment and crawl, the closer you get to becoming an expert crawler.
Start your journey with DeepCrawl
If you’re interested in running a crawl with DeepCrawl, discover our range of flexible plans or if you want to find out more about our platform simply drop us a message and we’ll get back to you asap.
Author
Sam Marsden
Sam Marsden is Deepcrawl’s Former SEO & Content Manager. Sam speaks regularly at marketing conferences, like SMX and BrightonSEO, and is a contributor to industry publications such as Search Engine Journal and State of Digital.

Frequently Asked Questions about how to crawl data from website using python

Can Python extract data from website?

Let’s say you find data from the web, and there is no direct way to download it, web scraping using Python is a skill you can use to extract the data into a useful form that can be imported.Jul 26, 2018

How do I crawl an entire website?

The six steps to crawling a website include:Configuring the URL sources.Understanding the domain structure.Running a test crawl.Adding crawl restrictions.Testing your changes.Running your crawl.

What is Web crawling in Python?

Web crawling is a powerful technique to collect data from the web by finding all the URLs for one or multiple domains. Python has several popular web crawling libraries and frameworks. In this article, we will first introduce different crawling strategies and use cases.Dec 11, 2020