#OrderedDict Methods

[webbazaar0101]

OrderedDict() collection:

OrderedDict() collection: Dictionary subclass that remembers the entry order. The OrderedDict() collection is similar to a dictionary object where keys maintain the order of insertion, whereas in the normal dictionary the order is arbitrary......

Dictionary subclass that remembers the entry order. The OrderedDict() collection is similar to a dictionary object where keys maintain the order of insertion, whereas in the normal dictionary the order is arbitrary. If we try to insert the key again, the previous value will be overwritten for that key. Syntax: collections.OrderDict() Example: import…

[iwebscrapingblogs]

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

[generatour1]

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[postalstreetaddress]

Postal Address Example in Python

You can use a postal-address object to store your address data. To retrieve this data, you should use the us address module, which uses advanced NLP methods. Its parse method splits up an unstructured address into its component parts and labels them. Then, you can use the tag method to return an OrderedDict with the labels as keys. This module also allows you to specify which fields you want to receive as response.

There are many options for identifying addresses with Python. In addition to finding matches between two strings, you can compare two addresses to determine whether they are identical. Although this method does not take into account the order of components, it is still effective enough to compare addresses with different formats.

The geocode() function supports single and multi-field formats. It can be called with one mandatory parameter, the address, or optional parameters to refine the search results. In addition to the address, you can pass a place name, POI, or single-line address. The more details you give, the more refined the results will be.

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[billypeterson449]

Python is a useful programming language for scripting, data science, and web development. In Python, a dictionary is an unordered collection of data values, similar to a map, that retains the key: value pair, unlike other data types that only hold a single value as an element. A dictionary stores its elements in key-value mapping style and uses hashing internally; as a result, we can rapidly retrieve a value from the dictionary by its key.

[jmtapio]

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:

# 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) response.data = { 'hits': response.data, } return response

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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