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[craigbrownphd]

#Technology #AI #ML #Analytics #Data #Cloud What is OrderedDict in Python? https://www.analyticsvidhya.com/blog/2024/06/ordereddict-in-python/?utm_source=dlvr.it&utm_medium=tumblr

[pythontipsnadtricks]

Built-in Modules in Python

Built-in modules in Python (learn python online) are pre-existing libraries that provide a wide range of functionalities to streamline and simplify various programming tasks. These modules are available as part of the standard Python library and cover a diverse array of areas, from mathematical calculations to working with files and managing dates. Here are some key built-in modules in Python:

math: This module offers mathematical functions, such as trigonometric, logarithmic, and arithmetic operations, providing access to mathematical constants like pi and e.

datetime: The datetime module allows manipulation and formatting of dates and times. It includes classes for working with dates, times, time intervals, and timezones.

random: The random module enables the generation of random numbers, providing functions for generating random integers, floating-point numbers, and random selections from sequences.

os: The os module offers a wide range of operating system-related functionalities. It provides functions for interacting with the file system, working with directories, and executing system commands.

sys: The sys module provides access to Python interpreter variables and functions, allowing interaction with the runtime environment. It's commonly used for handling command-line arguments and controlling the Python interpreter.

re: The re module is used for regular expression operations. It enables pattern matching and manipulation of strings based on specified patterns.

json: The json module facilitates encoding and decoding of JSON (JavaScript Object Notation) data, which is widely used for data interchange between applications.

collections: The collections module provides additional data structures beyond the built-in ones. It includes specialized container data types like OrderedDict, defaultdict, and namedtuple.

math: The math module contains mathematical functions and constants for more advanced calculations, including trigonometric, logarithmic, and exponential operations.

time: The time module provides functions for working with time-related tasks, including measuring execution times, setting timeouts, and creating timestamps.

csv: The csv module offers tools for reading and writing CSV (Comma-Separated Values) files, which are commonly used for tabular data storage.

heapq: The heapq module provides heap-related functions, allowing for the implementation of priority queues and heap-based algorithms.

These built-in modules save time and effort by providing pre-built solutions for common programming tasks. By utilizing these modules, developers can avoid reinventing the wheel and focus on creating more efficient and feature-rich applications.

[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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[codeparttime]

OrderedDict – Python Class | Why should we use it?

OrderedDict was developed to enable dictionaries to remember the insertion order. It is a subclass of the class dict.

[dritajapanese]

Autoprompt

#Autoprompt code

#Autoprompt license

10:31:19,694 - MainThread - botocore.hooks - DEBUG - Event -input-yaml: calling handler 10:31:19,694 - MainThread - botocore.hooks - DEBUG - Event -input-json: calling handler 10:31:19,694 - MainThread - awscli.arguments - DEBUG - Unpacked value of for parameter "target_group_arns": 10:31:19,694 - MainThread - botocore.hooks - DEBUG - Event -group-arns: calling handler 10:31:19,694 - MainThread - awscli.arguments - DEBUG - Unpacked value of 'awseb-e-txwhwjxk75-stack-AWSEBAutoScalingGroup-14CH3JZ6DNIPT' for parameter "auto_scaling_group_name": 'awseb-e-txwhwjxk75-stack-AWSEBAutoScalingGroup-14CH3JZ6DNIPT' 10:31:19,694 - MainThread - botocore.hooks - DEBUG - Event -load-balancer-target-groups: calling handler 10:31:19,693 - MainThread - botocore.hooks - DEBUG - Event -scaling-group-name: calling handler 10:31:19,687 - MainThread - botocore.hooks - DEBUG - Event -load-balancer-target-groups: calling handler > 10:31:19,687 - MainThread - botocore.hooks - DEBUG - Event -load-balancer-target-groups: calling handler 10:31:19,686 - MainThread - botocore.loaders - DEBUG - Loading JSON file: /usr/local/aws-cli/v2/2.0.10/dist/botocore/data/autoscaling//paginators-1.json 10:31:19,680 - MainThread - botocore.hooks - DEBUG - Event -load-balancer-target-groups: calling handler 10:31:19,680 - MainThread - awscli.clidriver - DEBUG - OrderedDict() 10:31:19,673 - MainThread - botocore.hooks - DEBUG - Event toscaling: calling handler 10:31:19,662 - MainThread - botocore.loaders - DEBUG - Loading JSON file: /usr/local/aws-cli/v2/2.0.10/dist/botocore/data/autoscaling//service-2.json 10:31:19,646 - MainThread - botocore.hooks - DEBUG - Event session-initialized: calling handler 10:31:19,644 - MainThread - botocore.hooks - DEBUG - Event session-initialized: calling handler 10:31:19,642 - MainThread - botocore.hooks - DEBUG - Event session-initialized: calling handler 10:31:19,641 - MainThread - botocore.hooks - DEBUG - Event session-initialized: calling handler 10:31:19,641 - MainThread - awscli.clidriver - DEBUG - Arguments entered to CLI: Aws cli version - aws-cli/1.18.52 Python/3.6.9 Linux/5.3.0-51-generic botocore/1.16.2Īws-cli/2.0.10 Python/3.7.3 Linux/5.3.0-51-generic aws -debug autoscaling attach-load-balancer-target-groups -auto-scaling-group-name awseb-e-txwhwjxk75-stack-AWSEBAutoScalingGroup-14CH3JZ6DNIPT -target-group-arns arn:aws:elasticloadbalancing:eu-central-1:063129209410:targetgroup/aifit-cc-qa/d9d8c7a85f2ea7df getLogger (_name_ ) 33 34 35 36 def loggers_handler_switcher (): 37 old_handlers = ' ) 232 return shlex. logger import PromptToolkitHandler 30 31 32 LOG = logging. factory import PromptToolkitFactory 29 from awscli. output import OutputGetter 28 from awscli. autocomplete import parser 25 from awscli. logger import LOG_FORMAT, disable_crt_logging 24 from awscli. document import Document 22 23 from awscli. completion import Completion 21 from prompt_toolkit. completion import Completer, ThreadedCompleter 20 from prompt_toolkit. application import Application 19 from prompt_toolkit. 13 import logging 14 import shlex 15 import sys 16 from contextlib import nullcontext, contextmanager 17 18 from prompt_toolkit.

#Autoprompt license

See the License for the specific 12 # language governing permissions and limitations under the License. This file is 10 # distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF 11 # ANY KIND, either express or implied. A copy of 5 # the License is located at 6 # 7 # 8 # 9 # or in the "license" file accompanying this file. You 4 # may not use this file except in compliance with the License. 2 # 3 # Licensed under the Apache License, Version 2.0 (the "License").

#Autoprompt code

As a special service "Fossies" has tried to format the requested source page into HTML format using (guessed) Python source code syntax highlighting (style: standard) with prefixed line numbers.Īlternatively you can here view or download the uninterpreted source code file.įor more information about "prompttoolkit.py" see the Fossies "Dox" file reference documentation.ġ # Copyright 2019, Inc.

[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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[techhelpnotes]

python – How can I sort a dictionary by key

Standard Python dictionaries are unordered (until Python 3.7). Even if you sorted the (key,value) pairs, you wouldnt be able to store them in a dict in a way that would preserve the ordering.

The easiest way is to use OrderedDict, which remembers the order in which the elements have been inserted:

[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.

[craigbrownphd]

What is OrderedDict in Python? https://www.analyticsvidhya.com/blog/2024/06/ordereddict-in-python/?utm_source=dlvr.it&utm_medium=tumblr

[webscreenscraping]

How Web Scraping Is Used To Extract Yahoo Finance Data: Stock Prices, Bids, Price Change And More?

The stock market is a massive database for technological companies, with millions of records that are updated every second! Because there are so many companies that provide financial data, it's usually done through Real-time web scraping API, and APIs always have premium versions. Yahoo Finance is a dependable source of stock market information. It is a premium version because Yahoo also has an API. Instead, you can get free access to any company's stock information on the website.

Although it is extremely popular among stock traders, it has persisted in a market when many large competitors, including Google Finance, have failed. For those interested in following the stock market, Yahoo provides the most recent news on the stock market and firms.

Steps to Scrape Yahoo Finance

Create the URL of the search result page from Yahoo Finance.

Download the HTML of the search result page using Python requests.

Scroll the page using LXML-LXML and let you navigate the HTML tree structure by using Xpaths. We have defined the Xpaths for the details we need for the code.

Save the downloaded information to a JSON file.+

We will extract the following data fields:

Previous close

Open

Bid

Ask

Day’s Range

52 Week Range

Volume

Average volume

Market cap

Beta

PE Ratio

1yr Target EST

You will need to install Python 3 packages for downloading and parsing the HTML file.

The Script

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)

Executing the Scraper

Assuming the script is named yahoofinance.py. If you type in the code name in the command prompt or terminal with a -h.

python3 yahoofinance.py -h usage: yahoo_finance.py [-h] ticker positional arguments: ticker optional arguments: -h, --help show this help message and exit

The ticker symbol, often known as a stock symbol, is used to identify a corporation.

To find Apple Inc stock data, we would make the following argument:

python3 yahoofinance.py AAPL

This will produce a JSON file named AAPL-summary.json in the same folder as the script.

This is what the output file would look like:

{ "Previous Close": "293.16", "Open": "295.06", "Bid": "298.51 x 800", "Ask": "298.88 x 900", "Day's Range": "294.48 - 301.00", "52 Week Range": "170.27 - 327.85", "Volume": "36,263,602", "Avg. Volume": "50,925,925", "Market Cap": "1.29T", "Beta (5Y Monthly)": "1.17", "PE Ratio (TTM)": "23.38", "EPS (TTM)": 12.728, "Earnings Date": "2020-07-28 to 2020-08-03", "Forward Dividend & Yield": "3.28 (1.13%)", "Ex-Dividend Date": "May 08, 2020", "1y Target Est": 308.91, "ticker": "AAPL", "url": "http://finance.yahoo.com/quote/AAPL?p=AAPL" }

This code will work for fetching the stock market data of various companies. If you wish to scrape hundreds of pages frequently, there are various things you must be aware of.

Why Perform Yahoo Finance Data Scraping?

If you're working with stock market data and need a clean, free, and trustworthy resource, Yahoo Finance might be the best choice. Different company profile pages have the same format, thus if you construct a script to scrape data from a Microsoft financial page, you could use the same script to scrape data from an Apple financial page.

If anyone is unable to choose how to scrape Yahoo finance data then it is better to hire an experienced web scraping company like Web Screen Scraping.

For any queries, contact Web Screen Scraping today or Request for a free Quote!!

[webbazaar0101]

Python Collection Module

Python Collection Module: The collections module provides specialized, high, performance alternatives for the built-in data types as well as a utility function to create named tuples. Python collections improve the functionalities of the built-in....

The collections module provides specialized, high, performance alternatives forthe built-in data types as well as a utility function to create named tuples. Python collections improve the functionalities of the built-in collection containers like list, dictionary, tuple etc. The following table lists the data types and operations of the collections module and their…

[tinchicus]

Python / OrderedDict

Python / OrderedDict , hoy les traigo una opcion interesante dict, espero les sea de utilidad!

Bienvenidos sean a este post, hoy hablaremos sobre este funcion del modulo de colecciones. Este tipo de dato es similar al diccionario (dict) pero a diferencia de este lleva un control de como se ingresaron los valores al mismo, esto nos es util cuando necesitamos recuperar la informacion por medio de una iteracion, para entender el concepto debemos trabajar con un ejemplo, vamos a crear un…