pyprobables is a pure-python library for probabilistic data structures. The goal is to provide the developer with a pure-python implementation of common probabilistic data-structures to use in their work.
To achieve better raw performance, it is recommended supplying an alternative hashing algorithm that has been compiled in C. This could include using the md5 and sha512 algorithms provided or installing a third party package and writing your own hashing strategy. Some options include the murmur hash mmh3 or those from the pyhash library. Each data object in pyprobables makes it easy to pass in a custom hashing function.
$ pip install pyprobables
To install from source:
To install pyprobables, simply clone the repository on GitHub, then run from the folder:
$ python setup.py install
pyprobables supports python 3.5 - 3.9+
For python 2.7 support, install release 0.3.2
$ pip install pyprobables==0.3.2
The documentation of is hosted on readthedocs.io
You can build the documentation locally by running:
$ pip install sphinx $ cd docs/ $ make html
To run automated tests, one must simply run the following command from the downloaded folder:
$ python setup.py test
from probables import (BloomFilter) blm = BloomFilter(est_elements=1000, false_positive_rate=0.05) blm.add('google.com') blm.check('facebook.com') # should return False blm.check('google.com') # should return True
from probables import (CountMinSketch) cms = CountMinSketch(width=1000, depth=5) cms.add('google.com') # should return 1 cms.add('facebook.com', 25) # insert 25 at once; should return 25
from probables import (CuckooFilter) cko = CuckooFilter(capacity=100, max_swaps=10) cko.add('google.com') cko.check('facebook.com') # should return False cko.check('google.com') # should return True
from probables import (BloomFilter) from probables.hashes import (default_sha256) blm = BloomFilter(est_elements=1000, false_positive_rate=0.05, hash_function=default_sha256) blm.add('google.com') blm.check('facebook.com') # should return False blm.check('google.com') # should return True
import mmh3 # murmur hash 3 implementation (pip install mmh3) from pyprobables.hashes import (hash_with_depth_bytes) from pyprobables import (BloomFilter) @hash_with_depth_bytes def my_hash(key): return mmh3.hash_bytes(key) blm = BloomFilter(est_elements=1000, false_positive_rate=0.05, hash_function=my_hash) import mmh3 # murmur hash 3 implementation (pip install mmh3) from pyprobables.hashes import (hash_with_depth_int) from pyprobables import (BloomFilter) @hash_with_depth_int def my_hash(key, encoding='utf-8'): max64mod = UINT64_T_MAX + 1 val = int(hashlib.sha512(key.encode(encoding)).hexdigest(), 16) return val % max64mod blm = BloomFilter(est_elements=1000, false_positive_rate=0.05, hash_function=my_hash)
Official Website: https://github.com/barrust/pyprobables
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.
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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
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 ’
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Welcome to my Blog, In this article, we will learn python lambda function, Map function, and filter function.
Lambda function in python: Lambda is a one line anonymous function and lambda takes any number of arguments but can only have one expression and python lambda syntax is
Syntax: x = lambda arguments : expression
Now i will show you some python lambda function examples:
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No programming language is pretty much as diverse as Python. It enables building cutting edge applications effortlessly. Developers are as yet investigating the full capability of end-to-end Python development services in various areas.
By areas, we mean FinTech, HealthTech, InsureTech, Cybersecurity, and that's just the beginning. These are New Economy areas, and Python has the ability to serve every one of them. The vast majority of them require massive computational abilities. Python's code is dynamic and powerful - equipped for taking care of the heavy traffic and substantial algorithmic capacities.
Programming advancement is multidimensional today. Endeavor programming requires an intelligent application with AI and ML capacities. Shopper based applications require information examination to convey a superior client experience. Netflix, Trello, and Amazon are genuine instances of such applications. Python assists with building them effortlessly.
Python can do such numerous things that developers can't discover enough reasons to admire it. Python application development isn't restricted to web and enterprise applications. It is exceptionally adaptable and superb for a wide range of uses.
Python is known for its tools and frameworks. There's a structure for everything. Django is helpful for building web applications, venture applications, logical applications, and mathematical processing. Flask is another web improvement framework with no conditions.
Web2Py, CherryPy, and Falcon offer incredible capabilities to customize Python development services. A large portion of them are open-source frameworks that allow quick turn of events.
Simple to read and compose
Python has an improved sentence structure - one that is like the English language. New engineers for Python can undoubtedly understand where they stand in the development process. The simplicity of composing allows quick application building.
The motivation behind building Python, as said by its maker Guido Van Rossum, was to empower even beginner engineers to comprehend the programming language. The simple coding likewise permits developers to roll out speedy improvements without getting confused by pointless subtleties.
Utilized by the best
Alright - Python isn't simply one more programming language. It should have something, which is the reason the business giants use it. Furthermore, that too for different purposes. Developers at Google use Python to assemble framework organization systems, parallel information pusher, code audit, testing and QA, and substantially more. Netflix utilizes Python web development services for its recommendation algorithm and media player.
Massive community support
Python has a steadily developing community that offers enormous help. From amateurs to specialists, there's everybody. There are a lot of instructional exercises, documentation, and guides accessible for Python web development solutions.
Today, numerous universities start with Python, adding to the quantity of individuals in the community. Frequently, Python designers team up on various tasks and help each other with algorithmic, utilitarian, and application critical thinking.
Python is the greatest supporter of data science, Machine Learning, and Artificial Intelligence at any enterprise software development company. Its utilization cases in cutting edge applications are the most compelling motivation for its prosperity. Python is the second most well known tool after R for data analytics.
The simplicity of getting sorted out, overseeing, and visualizing information through unique libraries makes it ideal for data based applications. TensorFlow for neural networks and OpenCV for computer vision are two of Python's most well known use cases for Machine learning applications.
Thinking about the advances in programming and innovation, Python is a YES for an assorted scope of utilizations. Game development, web application development services, GUI advancement, ML and AI improvement, Enterprise and customer applications - every one of them uses Python to its full potential.
The disadvantages of Python web improvement arrangements are regularly disregarded by developers and organizations because of the advantages it gives. They focus on quality over speed and performance over blunders. That is the reason it's a good idea to utilize Python for building the applications of the future.
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The opportunities big data offers also come with very real challenges that many organizations are facing today. Often, it’s finding the most cost-effective, scalable way to store and process boundless volumes of data in multiple formats that come from a growing number of sources. Then organizations need the analytical capabilities and flexibility to turn this data into insights that can meet their specific business objectives.
This Refcard dives into how a data lake helps tackle these challenges at both ends — from its enhanced architecture that’s designed for efficient data ingestion, storage, and management to its advanced analytics functionality and performance flexibility. You’ll also explore key benefits and common use cases.
As technology continues to evolve with new data sources, such as IoT sensors and social media churning out large volumes of data, there has never been a better time to discuss the possibilities and challenges of managing such data for varying analytical insights. In this Refcard, we dig deep into how data lakes solve the problem of storing and processing enormous amounts of data. While doing so, we also explore the benefits of data lakes, their use cases, and how they differ from data warehouses (DWHs).
This is a preview of the Getting Started With Data Lakes Refcard. To read the entire Refcard, please download the PDF from the link above.
#big data #data analytics #data analysis #business analytics #data warehouse #data storage #data lake #data lake architecture #data lake governance #data lake management