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iwebscrapingblogs · 4 years ago

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How To Scrape Expedia Using Python And LXML?

When done manually, gathering travel data for planes is a massive undertaking. There are various possible combinations of airports, routes, times, and costs, all of which are always changing. Ticket rates fluctuate on a daily (or even hourly) basis, and there are numerous flights available each day. Web scraping is one method for keeping track of this information. In this Blog, we'll scrape Expedia , a popular vacation booking site, to get flight information. The flight schedules and pricing for a sender and the receiver pair will be extracted by our scraper.

Data Fields that will be extracted:

Arrival Airport

Arrival Time

Departure Airport

Departure Time

Flight Name

Flight Duration

Ticket Price

No. Of Stops

Airline

Below shown is the screenshot of the data fields that we will be extracting:

Scraping Code:

1. Create the URL of the search results from Expedia for instance, we will check the available flights listed from New York to Miami:

https://www.expedia.com/Flights-Search?trip=oneway&leg1=from:New%20York,%20NY%20(NYC-All%20Airports),to:Miami,%20Florida,departure:04/01/2017TANYT&passengers=children:0,adults:1,seniors:0,infantinlap:Y&mode=search

2. Using Python Requests, download the HTML of the search result page.

3. Parse the webpage with LXML — LXML uses Xpaths to browse the HTML Tree Structure. The XPaths for the details we require in the code have already been defined.

4. Save the information in a JSON file. You can change this later to write to a database.

Requirements

We'll need several libraries for obtaining and parsing HTML for this Python 3 web scraping tutorial. The requirements for the package are shown below.

Install Python 3 and Pip

Install Packages

The code is explanatory

You can check the code from the link here.

Executing the Expedia Scraper

Let's say the script's name is expedia.py. In a command prompt or terminal, input the script name followed by a -h.

usage: expedia.py [-h] source destination date positional arguments: source Source airport code destination Destination airport code date MM/DD/YYYY optional arguments: -h, --help show this help message and exit

The input and output arguments are the airline codes for the source and destination airports, respectively. The date parameter must be in the form MM/DD/YYYY

For example, to get flights from New York to Miami, we would use the following arguments:

python3 expedia.py nyc mia 04/01/2017

The nyc-mia-flight-results.json file will be created as a result of this. json, which will be saved in the same directory as the script.

This is what the output file will look like:

{ "arrival": "Miami Intl., Miami", "timings": [ { "arrival_airport": "Miami, FL (MIA-Miami Intl.)", "arrival_time": "12:19a", "departure_airport": "New York, NY (LGA-LaGuardia)", "departure_time": "9:00p" } ], "airline": "American Airlines", "flight duration": "1 days 3 hours 19 minutes", "plane code": "738", "plane": "Boeing 737-800", "departure": "LaGuardia, New York", "stops": "Nonstop", "ticket price": "1144.21" }, { "arrival": "Miami Intl., Miami", "timings": [ { "arrival_airport": "St. Louis, MO (STL-Lambert-St. Louis Intl.)", "arrival_time": "11:15a", "departure_airport": "New York, NY (LGA-LaGuardia)", "departure_time": "9:11a" }, { "arrival_airport": "Miami, FL (MIA-Miami Intl.)", "arrival_time": "8:44p", "departure_airport": "St. Louis, MO (STL-Lambert-St. Louis Intl.)", "departure_time": "4:54p" } ], "airline": "Republic Airlines As American Eagle", "flight duration": "0 days 11 hours 33 minutes", "plane code": "E75", "plane": "Embraer 175", "departure": "LaGuardia, New York", "stops": "1 Stop", "ticket price": "2028.40" },

You can download the code at:

import json import requests from lxml import html from collections import OrderedDict import argparse def parse(source,destination,date): for i in range(5): try: url = "https://www.expedia.com/Flights-Search?trip=oneway&leg1=from:{0},to:{1},departure:{2}TANYT&passengers=adults:1,children:0,seniors:0,infantinlap:Y&options=cabinclass%3Aeconomy&mode=search&origref=www.expedia.com".format(source,destination,date) headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/70.0.3538.77 Safari/537.36'} response = requests.get(url, headers=headers, verify=False) parser = html.fromstring(response.text) json_data_xpath = parser.xpath("//script[@id='cachedResultsJson']//text()") raw_json =json.loads(json_data_xpath[0] if json_data_xpath else '') flight_data = json.loads(raw_json["content"]) flight_info = OrderedDict() lists=[] for i in flight_data['legs'].keys(): total_distance = flight_data['legs'][i].get("formattedDistance",'') exact_price = flight_data['legs'][i].get('price',{}).get('totalPriceAsDecimal','') departure_location_airport = flight_data['legs'][i].get('departureLocation',{}).get('airportLongName','') departure_location_city = flight_data['legs'][i].get('departureLocation',{}).get('airportCity','') departure_location_airport_code = flight_data['legs'][i].get('departureLocation',{}).get('airportCode','') arrival_location_airport = flight_data['legs'][i].get('arrivalLocation',{}).get('airportLongName','') arrival_location_airport_code = flight_data['legs'][i].get('arrivalLocation',{}).get('airportCode','') arrival_location_city = flight_data['legs'][i].get('arrivalLocation',{}).get('airportCity','') airline_name = flight_data['legs'][i].get('carrierSummary',{}).get('airlineName','') no_of_stops = flight_data['legs'][i].get("stops","") flight_duration = flight_data['legs'][i].get('duration',{}) flight_hour = flight_duration.get('hours','') flight_minutes = flight_duration.get('minutes','') flight_days = flight_duration.get('numOfDays','') if no_of_stops==0: stop = "Nonstop" else: stop = str(no_of_stops)+' Stop' total_flight_duration = "{0} days {1} hours {2} minutes".format(flight_days,flight_hour,flight_minutes) departure = departure_location_airport+", "+departure_location_city arrival = arrival_location_airport+", "+arrival_location_city carrier = flight_data['legs'][i].get('timeline',[])[0].get('carrier',{}) plane = carrier.get('plane','') plane_code = carrier.get('planeCode','') formatted_price = "{0:.2f}".format(exact_price) if not airline_name: airline_name = carrier.get('operatedBy','') timings = [] for timeline in flight_data['legs'][i].get('timeline',{}): if 'departureAirport' in timeline.keys(): departure_airport = timeline['departureAirport'].get('longName','') departure_time = timeline['departureTime'].get('time','') arrival_airport = timeline.get('arrivalAirport',{}).get('longName','') arrival_time = timeline.get('arrivalTime',{}).get('time','') flight_timing = { 'departure_airport':departure_airport, 'departure_time':departure_time, 'arrival_airport':arrival_airport, 'arrival_time':arrival_time } timings.append(flight_timing) flight_info={'stops':stop, 'ticket price':formatted_price, 'departure':departure, 'arrival':arrival, 'flight duration':total_flight_duration, 'airline':airline_name, 'plane':plane, 'timings':timings, 'plane code':plane_code } lists.append(flight_info) sortedlist = sorted(lists, key=lambda k: k['ticket price'],reverse=False) return sortedlist except ValueError: print ("Rerying...") return {"error":"failed to process the page",} if __name__=="__main__": argparser = argparse.ArgumentParser() argparser.add_argument('source',help = 'Source airport code') argparser.add_argument('destination',help = 'Destination airport code') argparser.add_argument('date',help = 'MM/DD/YYYY') args = argparser.parse_args() source = args.source destination = args.destination date = args.date print ("Fetching flight details") scraped_data = parse(source,destination,date) print ("Writing data to output file") with open('%s-%s-flight-results.json'%(source,destination),'w') as fp: json.dump(scraped_data,fp,indent = 4)

Unless the page structure changes dramatically, this scraper should be able to retrieve most of the flight details present on Expedia. This scraper is probably not going to work for you if you want to scrape the details of thousands of pages at very short intervals.

Contact iWeb Scraping for extracting Expedia using Python and LXML or ask for a free quote!

https://www.iwebscraping.com/how-to-scrape-expedia-using-python-and-lxml.php

#ScrapingDatafromExpedia #ScrapeExpediaWebsite #ExpediaDataExtraction

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generatour1 · 5 years ago

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webscreenscraping · 4 years ago

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How To Scrape Stock Market Data Using Python?

The coronavirus pandemic has proved that the stock market is also volatile like all other business industries as it may crash within seconds and may also skyrocket in no time! Stocks are inexpensive at present because of this crisis and many people are involved in getting stock market data for helping with the informed options.

Unlike normal web scraping, extracting stock market data is much more particular and useful to people, who are interested in stock market investments.

Web Scraping Described

Web scraping includes scraping the maximum data possible from the preset indexes of the targeted websites and other resources. Companies use web scraping for making decisions and planning tactics as it provides viable and accurate data on the topics.

It's normal to know web scraping is mainly associated with marketing and commercial companies, however, they are not the only ones, which benefit from this procedure as everybody stands to benefit from extracting stock market information. Investors stand to take benefits as data advantages them in these ways:

Investment Possibilities

Pricing Changes

Pricing Predictions

Real-Time Data

Stock Markets Trends

Using web scraping for others’ data, stock market data scraping isn’t the coolest job to do but yields important results if done correctly. Investors might be given insights on different parameters, which would be applicable for making the finest and coolest decisions.

Scraping Stock Market and Yahoo Finance Data with Python

Initially, you’ll require installing Python 3 for Mac, Linux, and Windows. After that, install the given packages to allow downloading and parsing HTML data: and pip for the package installation, a Python request package to send requests and download HTML content of the targeted page as well as Python LXML for parsing with the Xpaths.

Python 3 Code for Scraping Data from Yahoo Finance

from lxml import html import requests import json import argparse from collections import OrderedDict def get_headers(): \ return {"accept": "text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.9", \ "accept-encoding": "gzip, deflate, br", \ "accept-language": "en-GB,en;q=0.9,en-US;q=0.8,ml;q=0.7", \ "cache-control": "max-age=0", \ "dnt": "1", \ "sec-fetch-dest": "document", \ "sec-fetch-mode": "navigate", \ "sec-fetch-site": "none", \ "sec-fetch-user": "?1", \ "upgrade-insecure-requests": "1", \ "user-agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/81.0.4044.122 Safari/537.36"} def parse(ticker): \ url = "http://finance.yahoo.com/quote/%s?p=%s" % (ticker, ticker) \ response = requests.get( \ url, verify=False, headers=get_headers(), timeout=30) \ print("Parsing %s" % (url)) \ parser = html.fromstring(response.text) \ summary_table = parser.xpath( \ '//div[contains(@data-test,"summary-table")]//tr') \ summary_data = OrderedDict() \ other_details_json_link = "https://query2.finance.yahoo.com/v10/finance/quoteSummary/{0}?formatted=true&lang=en-US®ion=US&modules=summaryProfile%2CfinancialData%2CrecommendationTrend%2CupgradeDowngradeHistory%2Cearnings%2CdefaultKeyStatistics%2CcalendarEvents&corsDomain=finance.yahoo.com".format( \ ticker) \ summary_json_response = requests.get(other_details_json_link) \ try: \ json_loaded_summary = json.loads(summary_json_response.text) \ summary = json_loaded_summary["quoteSummary"]["result"][0] \ y_Target_Est = summary["financialData"]["targetMeanPrice"]['raw'] \ earnings_list = summary["calendarEvents"]['earnings'] \ eps = summary["defaultKeyStatistics"]["trailingEps"]['raw'] \ datelist = [] \ for i in earnings_list['earningsDate']: \ datelist.append(i['fmt']) \ earnings_date = ' to '.join(datelist) \ for table_data in summary_table: \ raw_table_key = table_data.xpath( \ './/td[1]//text()') \ raw_table_value = table_data.xpath( \ './/td[2]//text()') \ table_key = ''.join(raw_table_key).strip() \ table_value = ''.join(raw_table_value).strip() \ summary_data.update({table_key: table_value}) \ summary_data.update({'1y Target Est': y_Target_Est, 'EPS (TTM)': eps, \ 'Earnings Date': earnings_date, 'ticker': ticker, \ 'url': url}) \ return summary_data \ except ValueError: \ print("Failed to parse json response") \ return {"error": "Failed to parse json response"} \ except: \ return {"error": "Unhandled Error"} if __name__ == "__main__": \ argparser = argparse.ArgumentParser() \ argparser.add_argument('ticker', help='') \ args = argparser.parse_args() \ ticker = args.ticker \ print("Fetching data for %s" % (ticker)) \ scraped_data = parse(ticker) \ print("Writing data to output file") \ with open('%s-summary.json' % (ticker), 'w') as fp: \ json.dump(scraped_data, fp, indent=4)

Real-Time Data Scraping

As the stock market has continuous ups and downs, the best option is to utilize a web scraper, which scrapes data in real-time. All the procedures of data scraping might be performed in real-time using a real-time data scraper so that whatever data you would get is viable then, permitting the best as well as most precise decisions to be done.

Real-time data scrapers are more costly than slower ones however are the finest options for businesses and investment firms, which rely on precise data in the market as impulsive as stocks.

Advantages of Stock Market Data Scraping

All the businesses can take advantage of web scraping in one form particularly for data like user data, economic trends, and the stock market. Before the investment companies go into investment in any particular stocks, they use data scraping tools as well as analyze the scraped data for guiding their decisions.

Investments in the stock market are not considered safe as it is extremely volatile and expected to change. All these volatile variables associated with stock investments play an important role in the values of stocks as well as stock investment is considered safe to the extent while all the volatile variables have been examined and studied.

To collect as maximum data as might be required, you require to do stock markets data scraping. It implies that maximum data might need to be collected from stock markets using stock market data scraping bots.

This software will initially collect the information, which is important for your cause as well as parses that to get studied as well as analyzed for smarter decision making.

Studying Stock Market with Python

Jupyter notebook might be utilized in a course of the tutorial as well as you can have it on GitHub.

Setup Procedure

You will start installing jupyter notebooks because you have installed Anaconda

Along with anaconda, install different Python packages including beautifulsoup4, fastnumbers, and dill.

Add these imports to the Python 3 jupyter notebooks

import numpy as np # linear algebra import pandas as pd # pandas for dataframe based data processing and CSV file I/O import requests # for http requests from bs4 import BeautifulSoup # for html parsing and scraping import bs4 from fastnumbers import isfloat from fastnumbers import fast_float from multiprocessing.dummy import Pool as ThreadPool import matplotlib.pyplot as plt import seaborn as sns import json from tidylib import tidy_document # for tidying incorrect html sns.set_style('whitegrid') %matplotlib inline from IPython.core.interactiveshell import InteractiveShell InteractiveShell.ast_node_interactivity = "all"

What Will You Require Extract the Necessary Data?

Remove all the excessive spaces between the strings

A few strings from the web pages are available with different spaces between words. You may remove it with following:

def remove_multiple_spaces(string): \ if type(string)==str: \ return ' '.join(string.split()) \ return string

Conversions of Strings that Float

In different web pages, you may get symbols mixed together with numbers. You could either remove symbols before conversions, or you utiize the following functions:

def ffloat_list(string_list): \ return list(map(ffloat,string_list))

Sending HTTP Requests using Python

Before making any HTTP requests, you require to get a URL of a targeted website. Make requests using requests.get, utilize response.status_code for getting HTTP status, as well as utilize response.content for getting a page content.

Scrape and Parse the JSON Content from the Page

Scrape json content from the page with response.json() and double check using response.status_code.

Scraping and Parsing HTML Data

For that, we would use beautifulsoup4 parsing libraries.

Utilize Jupyter Notebook for rendering HTML Strings

Utilize the following functions:

from IPython.core.display import HTML HTML("Rendered HTML")

Find the Content Positions with Chrome Inspector

You’ll initially require to know HTML locations of content that you wish to scrape before proceeding. Inspect a page with Mac or chrome inspector using the functions cmd+option+I as well as inspect for the Linux using a function called Control+Shift+I.

Parse a Content and Show it using BeautifulSoup4

Parse a content with a function BeautifulSoup as well as get content from the header 1 tag as well as render it.

A web scraper tool is important for investment companies and businesses, which need to buy stocks as well as make investments in stock markets. That is because viable and accurate data is required to make the finest decisions as well as they could only be acquired by scraping and analyzing the stock markets data.

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rafi1228 · 5 years ago

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Dictionaries, Sets, and Related Data Structures

What you’ll learn

Associative Arrays

Hash Tables and Hash Functions

Python’s implementation of hash tables

Dictionaries and Sets

Defining hash functions for our custom classes and why that is useful

Creating customized dictionaries using the UserDict class

defaultdict

OrderedDict and Python3.6+ equivalences

Counter (multi-sets)

ChainMap

Serialization and Deserialization

JSON serialization/deserialization

Intro to JSONSchema, Marshmallow, PyYaml and Serpy 3rd party libraries

Requirements

This is an advanced course, so a solid Python foundation is necessary

Jupyter Notebooks

functional programming (zip, map, sorted, any, all, etc)

lambdas, closures and decorators

built-in decorators such as @lru_cache, @singledispatch and @wraps

iterables, iterators, generators and context managers

variable scopes and namespaces (globals, locals, etc)

ability to install 3rd party libraries (e.g. pip install)

Description

This course is an in-depth look at Python dictionaries.

Dictionaries are ubiquitous in Python. Classes are essentially dictionaries, modules are dictionaries, namespaces are dictionaries, sets are dictionaries and many more.

In this course we’ll take an in-depth look at:

associative arrays and how they can be implemented using hash maps

hash functions and how we can leverage them for our own custom classes

Python dictionaries and sets and the various operations we can perform with them

specialized dictionary structures such as OrderedDict and how it relates to the built-in Python3.6+ dict

Python’s implementation of multi-sets, the Counter class

the ChainMap class

how to create custom dictionaries by inheriting from the UserDict class

how to serialize and deserialize dictionaries to JSON

the use of schemas in custom JSON deserialization

a brief introduction to some useful libraries such as JSONSchema, Marshmallow, PyYaml and Serpy

***** Prerequisites *****

Please note that this is a relatively advanced Python course, and a strong knowledge of some topics in Python is required.

Beyond the basics of Python (loops, conditional statements, exception handling, built-in data types, creating classes, etc), you should also have an in-depth understanding of the following topics:

functions and functional programming (recursion, *args, **kwargs, zip, map, sorted, any, all, etc)

lambdas, closures and decorators (including standard decorators such as @singledispatch, @wraps, etc)

iterables, iterators, generators and context managers

named tuples

variable scopes and namespaces (globals, locals, etc)

For this course you will also need to install some 3rd party libraries, so you need to be comfortable with doing this using the tool of your choice (e.g. pip, conda, etc)

Finally, most of the code in this course is illustrated using the freely available Jupyter Notebooks, so you will need that as well.

Who this course is for:

Python developers who want a deeper understanding of Python dictionaries and related topics

Created by Fred Baptiste Last updated 12/2018 English

Size: 9.82 GB

Download Now

https://ift.tt/2xkhDEX.

The post Python 3: Deep Dive (Part 3) appeared first on Free Course Lab.

#IFTTT #Blogger

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jmtapio · 7 years ago

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11 May 2018 Python, Django

By default, when you hook up a model to Django REST Framework and run a query in JSON format, what you get is a list. E.g.

For GET localhost:8000/api/mymodel/

[ {"id": 1, "name": "Foo"}, {"id": 2, "name": "Bar"}, {"id": 3, "name": "Baz"} ]

This isn't great because there's no good way to include other auxiliary data points that are relevant to this query. In Elasticsearch you get something like this:

{ "took": 106, "timed_out": false, "_shards": {}, "hits": { "total": 0, "hits": [], "max_score": 1 } }

Another key is that perhaps today you can't think of any immediate reason why you want to include some additonal meta data about the query, but perhaps some day you will.

The way to solve this in Django REST Framework is to override the list function in your Viewset classes.

Before

# views.py # views.py from rest_framework import viewsets class BlogpostViewSet(viewsets.ModelViewSet): queryset = Blogpost.objects.all().order_by('date') serializer_class = serializers.BlogpostSerializer

After

# views.py from rest_framework import viewsets class BlogpostViewSet(viewsets.ModelViewSet): queryset = Blogpost.objects.all().order_by('date') serializer_class = serializers.BlogpostSerializer def list(self, request, *args, **kwargs): response = super().list(request, *args, **kwargs) # Where the magic happens! return response

Now, to re-wrap that, the response.data is a OrderedDict which you can change. Here's one way to do it:

And if you want to do the same the "detail API" where you retrieve a single model instance, you can add an override to the retrieve method:

def retrieve(self, request, *args, **kwargs): response = super().retrieve(request, *args, **kwargs) response.data = { 'hit': response.data, } return response

That's it. Perhaps it's personal preference but if you, like me, prefers this style, this is how you do it. I like namespacing things instead of dealing with raw lists.

"Namespaces are one honking great idea -- let's do more of those!"

From import this

Note! This works equally when you enable pagination. Enabling pagination immediately changes the main result from a list to a dictionary. I.e. Instead of...

[ {"id": 1, "name": "Foo"}, {"id": 2, "name": "Bar"}, {"id": 3, "name": "Baz"} ]

you now get...

{ "count": 3, "next": null, "previous": null, "items": [ {"id": 1, "name": "Foo"}, {"id": 2, "name": "Bar"}, {"id": 3, "name": "Baz"} ] }

So if you apply the "trick" mentioned in this blog post you end up with...:

{ "hits": { "count": 3, "next": null, "previous": null, "items": [ {"id": 1, "name": "Foo"}, {"id": 2, "name": "Bar"}, {"id": 3, "name": "Baz"} ] } }

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