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.

What is Screen Scraping and How Does it Work? – SearchDataCenter

Screen scraping is the act of copying information that shows on a digital display so it can be used for another purpose. Visual data can be collected as raw text from on-screen elements such as a text or images that appear on the desktop, in an application or on a website. Screen scraping can be performed automatically with a scraping program or manually with an individual extracting data.
Screen scraping has a variety of uses, both ethical and unethical. Brief examples of both include either an app for banking, for gathering data from multiple accounts for a user, or for stealing data from applications. A developer might be tempted to steal code from another application to make the process of development faster and easier for themselves.
What is it used for?
Screen scrapers have been applied in a broad number of fields for a variety of use cases. Some potential uses include:
banking applications and financial transactions;
saving meaningful data for later use;
to perform actions a user would on a website;
to translate data from a legacy application to a modern application;
for data aggregators such as price comparison websites;
to track user profiles to see online activities; and
to steal data.
One of the largest use cases has been in banking. Lenders may want to use screen scraping to gather a customer’s financial data. Financial-based applications may use screen scraping to access multiple accounts from a user, aggregating all the information in one place. Users would need to explicitly trust the application, however, as they are trusting that organization with their accounts, customer data and passwords. Screen scraping can also be used for mortgage provider applications.
An organization might also want to use screen scraping to translate between legacy application programs and new user interfaces (UIs) so that the logic and data associated with the legacy programs can continue to be used. This option is rarely used and is only seen as an option when other methods are impractical.
If an individual can gain access to the underlying code in an application, the user could use screen scraping to steal the code and use it in their own application. This would save the individual time and effort or allow them to learn how a feature in an application works without permission.
A portion of the time, screen scraping will involve a third-party system. For example, screen scraping would allow a third-party organization to access data on financial transactions in a budgeting app.
Screen scraping has changed its main use cases over time. A recent example of this comes from 2019 when screen scraping began to be phased out of one of its larger use cases, banking. This was done to ease security concerns surrounding the practice. Budgeting apps now must use a single, open banking technology.
How does screen scraping work?
Screen scraping can be accomplished in several ways, depending on what the process is being used for. For example, through Java, an individual can copy and paste source code from one application into their own if they have a pathway of direct access to it.
In general, screen scraping allows a user to extract screen display data from a specific UI element or documents. Different methods can be used to obtain all the text on a page, unformatted, or all the text on a page, formatted, with exact positioning. Screen scrapers can be based around applications such as Selenium or PhantomJS, which allows users to obtain information from HTML in a browser. Unix tools, such as Shell scripts, can also be used as a simple screen scraper.
In banking, a third-party will request users share their login information so they can access financial transaction data by logging into digital portals for the customers. A budgeting app can then retrieve the incoming and outgoing transactions across accounts.
Regarding the use of transferring data from a legacy program, a data scraping program must take the data coming from the legacy program that is formatted for the screen of an older type of terminal such as an IBM 3270 display and reformat it for Windows 10 or someone using a web browser. The program must also reformat user input from the newer user interfaces (such as a Windows graphical user interface or a web browser) so that the request can be handled by the legacy application as if it came from the user of the older device and user interface.
How to prevent screen scraping
Unfortunately, there is no one definitive way to prevent screen scraping from happening. However, there are ways to help deter it from happening. An organization can detect screen scraping through a few given signatures or use behaviors. For example, if a nonstandard user agent is detected, if JavaScript fails to run client-side or several page request sequences are made, it may be a sign of screen scraping.
To help deter screen scaping, an organization can:
use one-time passwords, because screen scrapers will not be able to see a password until it is used;
use web application firewalls, which can help detect signature- or behavior-based actions;
set a cookie value to be checked by the webserver in JavaScript;
make sure endpoints or APIs aren’t exposed;
run fraud detection software to catch screen scraping potentially while it is happening; and/or
set content to be shown as an image, which won’t stop screen scraping from happening but will stop programs that can’t translate images.
All these methods can help deter screen scraping, but it won’t stop it completely. In addition, organizations must make sure that their actions won’t make the end-user experience worse. For example, setting a website’s content to appear as an image may make it difficult for individuals to find the page, because it will affect how search engines find the page to begin with.
Screen scraping tools
If individuals don’t want to screen scrape manually, there are several tools that can help automate the process, such as:
UiPath
Jacada
FMiner
Macro Scheduler
ScreenScraper Studio
Existek
These tools include automation features such as automated user interfaces, macro recorders and editors. They work with Windows or web applications. Some tools have specific features over others and focus on specific platforms.
Screen scraping vs. web scraping
While screen scraping is the process of extracting data shown on a screen, web scraping extracts data from the web. The two concepts share many similarities to the point where it can be said that web scraping is like a specific type of screen scraping. The main differences lie in where the data is being taken from and what is it being used for.
Web scraping is used to extract data exclusively from the web — unlike screen scraping, which can also scrape data from a user’s desktop or applications. This form of data extraction can be used to compare prices for goods on an e-commerce shop, for web indexing and data mining.
The process accesses the web through HTTP over a web browser and can either be done manually or automatically through a bot or web crawler.
Difference between screen scraping and data scraping
Data scraping is a variant of screen scraping that is used to copy data from documents and web applications. Data scraping is a technique where structured, human-readable data is extracted. This method is mostly used for exchanging data with a legacy system and making it readable by modern applications.
Screen scraping and open banking
Open banking is the concept of sharing secured financial information to be used by third-party developers for the creation of banking applications. This concept is based on the sharing of APIs, which allows an application to use the same API to aggregate information from different accounts into one place. This is what allows a banking app to let users look at their multiple accounts from different banks in one place.
In the past, some banking apps would gather information using screen scraping. This process would require a user to share their bank logon credentials to the third-party app. The application would then log on to the user’s accounts on his or her behalf and screen scrape the needed data to show in-app.
By contrast, open banking now uses shared APIs, meaning the exact data needed is copied without requiring the user to share logon credentials. The concept was introduced in 2018 and is now becoming a standard over the use of screen scraping.
This was last updated in February 2020
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Frequently Asked Questions about how to screen scrape with python

What is screen scraping in Python?

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.Jul 26, 2018

How do you screen scrape?

Screen scraping is the act of copying information that shows on a digital display so it can be used for another purpose. Visual data can be collected as raw text from on-screen elements such as a text or images that appear on the desktop, in an application or on a website.

How do I scrape data from a website using python windows?

Let’s get started scraping using Python3 and the library BeautifulSoup!Step 0: Install Python version 3. … Step 1: Choose a website that you want to scrape . … Step 2: Install Python packages (request & beautifulSoup) … Step 3: Create a python file. … Step 4: Create the scraping code. … Step 5: Run the program.More items…