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
Welcome to my Blog , In this article, you are going to learn the top 10 python tips and tricks.
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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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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.
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