Install and update using
pip install -U botflow
All functions are connected by pipes (queues) and communicate by data.
When data come in, the function will be called and return the result.
Think about the pipeline operation in unix:
Botflow provides pipe and route. It makes dataflow programming and powerful data flow processes easier.
Botflow is easy to use and maintain, does not need configuration files, and knows about
asyncio and how to parallelize computation.
Here's one of the simple applications you can make:
_Load the price of Bitcoin every 2 seconds. Advantage price aggregator sample can be found here.
from botflow import * def main(): Pipe( Timer(delay=2), # send timer data to pipe every 2 seconds "http://api.coindesk.com/v1/bpi/currentprice.json", # send url to pipe when timer trigger HttpLoader(), # read url and load http response lambda r: r.json['bpi']['USD']['rate_float'], # read http response and parse as json print, # print out ) Bot.render('ex_output/simple_bitcoin_price') Bot.run() main()
Or write in chain style
from botflow import * p_cd_bitcoin=Pipe().Timer(delay=2).Loop("http://api.coindesk.com/v1/bpi/currentprice.json")\ .HttpLoader().Map(lambda r: r.json['bpi']['USD']['rate_float']).Map(print) p_cd_bitcoin.run()
With render function: Bot.render('bitcoin_arbitrage') Botflow will render the data flow network into a graphviz image. below is the flow graph generated by Botflow.Aggreate 6 exchanges bitcoin price for trading.
Nodes will be run in parallel, and they will perform well when processing stream data. :Web Crawle: Botflow is 10x fatter than Scrapy
With replay mode enabled:
config.replay_mode=True when an exception is raised at step N, you don't need to run from setup 1 to N. Botflow will replay the data from nearest completed node, usually step N-1. It will save a lot of time in the development phase.
# Jupyter support. Able to run inside Jupyter note book.
# pipe can be nest in another Pipe.
p1=Pipe(get_image) p2=Pipe(get_price) p_get_all=Pipe(Zip(p1,p2)).Filter
# Support Chain style pipe line creating.
Major change see below .:
# Backpressure rate limit support
# Httpserver support
# new Node support. Zip, SendTo Flat for make loop and redirect the flow
# Type hints support .for function type route
# reorge the source code for readable.
Data- programming is typically applied to streams of structured data for filtering, transforming, aggregating (such as computing statistics), or calling other programs.
Botflow has a few basic concepts to implement Dataflow programming .
All units (Pipe, Node, Route) communicate via queues and perform parallel computation in coroutines. This is abstracted so that Botflow can be used with only limited knowledge of
It will be used to create a complex data flow network, not just one main process. Botflow can nest Routes inside Routes. It is a powerful concept. There are some pre built-in Route:
It is callable unit.Any callable function and object can work as Node. It is driven by data. Custom functions work as Map unit. There are some built-in nodes:
It is feed stream data to the pipe.
Source Code: https://github.com/kkyon/botflow
License: View license
If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.
If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.
In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.
#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition
At the end of 2019, Python is one of the fastest-growing programming languages. More than 10% of developers have opted for Python development.
In the programming world, Data types play an important role. Each Variable is stored in different data types and responsible for various functions. Python had two different objects, and They are mutable and immutable objects.
Table of Contents hide
III Built-in data types in Python
The Size and declared value and its sequence of the object can able to be modified called mutable objects.
Mutable Data Types are list, dict, set, byte array
The Size and declared value and its sequence of the object can able to be modified.
Immutable data types are int, float, complex, String, tuples, bytes, and frozen sets.
id() and type() is used to know the Identity and data type of the object
a**=str(“Hello python world”)****#str**
Numbers are stored in numeric Types. when a number is assigned to a variable, Python creates Number objects.
Python supports 3 types of numeric data.
int (signed integers like 20, 2, 225, etc.)
float (float is used to store floating-point numbers like 9.8, 3.1444, 89.52, etc.)
complex (complex numbers like 8.94j, 4.0 + 7.3j, etc.)
A complex number contains an ordered pair, i.e., a + ib where a and b denote the real and imaginary parts respectively).
The string can be represented as the sequence of characters in the quotation marks. In python, to define strings we can use single, double, or triple quotes.
# String Handling
#single (') Quoted String
# Double (") Quoted String
# triple (‘’') (“”") Quoted String
In python, string handling is a straightforward task, and python provides various built-in functions and operators for representing strings.
The operator “+” is used to concatenate strings and “*” is used to repeat the string.
'Output : Python python ’
#python web development #data types in python #list of all python data types #python data types #python datatypes #python types #python variable type
Students will be able to know various commands include:
Python Basic Programming
string,if else,Range, for loop, while loop, making functions, tuple, dictionary etc
Download files from Web, Exceptional Handling etc
Get data from files create , read, write, delete.
IMAGE PROCESSING: (NEW DEVELOPING ART)
Image transformation , Image crop , edit and much more
Listener should know how about very basic C Language basic commands to understand Python Language
What will you learn
I will more update if any student want need to add more skills related to python
#Python Programming & Data Handling #Python Programming #Data Handling #Programming # Python
Alright is a python wrapper that helps you automate WhatsApp web using python, giving you the capability to send messages, images, video, and files to both saved and unsaved contacts without having to rescan the QR code every time.
I was looking for a way to control and automate WhatsApp web with Python; I came across some very nice libraries and wrappers implementations, including:
So I tried
pywhatkit, a well crafted to be used, but its implementations require you to open a new browser tab and scan QR code every time you send a message, no matter if it’s the same person, which was a deal-breaker for using it.
I then tried
which is based onyowsupand require you to do some registration with
yowsupbefore using it of which after a bit of googling, I got scared of having my number blocked. So I went for the next option.
I then went for WebWhatsapp-Wrapper. It has some good documentation and recent commits so I had hoped it is going to work. But It didn’t for me, and after having a couple of errors, I abandoned it to look for the next alternative.
PyWhatsapp by shauryauppal, which was more of a CLI tool than a wrapper, surprisingly worked. Its approach allows you to dynamically send WhatsApp messages to unsaved contacts without rescanning QR-code every time.
So what I did is refactoring the implementation of that tool to be more of a wrapper to easily allow people to run different scripts on top of it. Instead of just using it as a tool, I then thought of sharing the codebase with people who might struggle to do this as I did.
#python #python-programming #python-tutorials #python-programming-lists #selenium #python-dev-tips #python-developers #programming #web-monetization
Python is the most widespread and popular programming language in data science, software development, and related fields. The simplicity of codes in Python, which helps learners avoid any confusion, is the key to this popularity. Python has constantly been developing, and it keeps getting updated for more ease in using. With 137,000 plus libraries and tools, Python has always provided its users with the solutions to problems of any complexity level. This reason makes Python the ideal language for Data Science operations. This article focuses on some of the essential and must-learn libraries in Python used heavily by Data Scientists. I have tried to cover different libraries used in various stages of a data science cycle, such as Data Mining, processing and modeling, Data Visualization.
Learn Data Science in Python from here!
#data-visualization #data #data-science #python-programming #python #must-know data science libraries in python