webscraping.proIn this post we share how to plot distribution histogram for the Weibull ditribution and the distribution of sample averages as approximated by the Normal (Gaussian) distribution. We’ll show how the approximation accuracy changes with samples volume increase.
One may get the full .ipynb file here.
import sys
import requests
URL = 'https://portal.bitcasa.com/login'
client = requests.session()
# Retrieve the CSRF token first
client.get(URL) # sets cookie
if 'csrftoken' in client.cookies:
# Django 1.6 and up
csrftoken = client.cookies['csrftoken']
else:
# older versions
csrftoken = client.cookies['csrf']
# Pass CSRF token both in login parameters (csrfmiddlewaretoken)
# and in the session cookies (csrf in client.cookies)
login_data = dict(username=EMAIL, password=PASSWORD, csrfmiddlewaretoken=csrftoken, next='/')
r = client.post(URL, data=login_data, headers=dict(Referer=URL))The JS loading page is usually scraped by Selenium or another browser emulator. Yet, for a certain shopping website we’ve
found a way to perform a pure Python requests scrape.
We want to share with you how to scrape text and store it as Pandas data frame using BeautifulSoup (Python). The code below works to store html li items in the ‘engine, ‘trans’, ‘colour’ and ‘interior’ columns.
from bs4 import BeautifulSoup
import pandas as pd
import requests
main_url = "https://www.example.com/"
def getAndParseURL(url):
result = requests.get(url)
soup = BeautifulSoup(result.text, 'html.parser')
return(soup)
soup = getAndParseURL(main_url)
ul = soup.select('ul[class="list-inline lot-breakdown-list"] li', recursive=True)
lis_e = []
for li in ul:
lis = []
lis.append(li.contents[1])
lis_e.extend(lis)
engine.append(lis_e[0])
trans.append(lis_e[1])
colour.append(lis_e[2])
interior.append(lis_e[3])
scraped_data = pd.DataFrame({'engine': engine,
'transmission': trans, 'colour': colour,
'interior': interior})
The code was provided by Ahmed Soliman.
I recently got a question and it looked like this : how to download a file from a link in Python?
“I need to go to every link which will open a website and that would have the download file “Export offers to XML”. This link is javascript enabled.”
We’ve done the Linkedin scraper that downloades the free study courses. They include text data, exercise files and 720HD videos. The code does not represent the pure Linkedin scraper, a business directory data extractor. Yet, you might grasp the main thoughts and useful techniques for your Linkedin scraper development. 
Recently, I was challenged to do bulk submits through an authenticated form. The website required a login. While there are plenty of examples of how to use POST and GET in Python, I want to share with you how I handled the session along with a cookie and authenticity token (CSRF-like protection).
In the post, we are going to cover the crucial techniques needed in the scripting web scraping:
I often receive requests asking about email crawling. It is evident that this topic is quite interesting for those who want to scrape contact information from the web (like direct marketers), and previously we have already mentioned GSA Email Spider as an off-the-shelf solution for email crawling. In this article I want to demonstrate how easy it is to build a simple email crawler in Python. This crawler is simple, but you can learn many things from this example (especially if you’re new to scraping in Python).
test.py
import MySQLdb, db_config
class Test:
def connect(self):
self.conn = MySQLdb.connect(host=config.db_credentials["mysql"]["host"],
user=config.db_credentials["mysql"]["user"],
passwd=config.db_credentials["mysql"]["pass"],
db=config.db_credentials["mysql"]["name"])
self.conn.autocommit(True)
return self.conn
def insert_parametrized(self, test_value="L'le-Perrot"):
cur = self.connect().cursor()
cur.execute("INSERT INTO a_table (name, city) VALUES (%s,%s)", ('temp', test_value))
# run it
t=Test().insert_parametrized("test city'; DROP TABLE a_table;")
db_config.py (place it in the same directory as the test.py file)
db_credentials = {
"mysql": {
"name": "db_name",
"host": "db_host", # eg. '127.0.0.1'
"user": "xxxx",
"pass": "xxxxxxxx",
}
}