fast-python | Source code for Fast Python by Chris Conlan

by chrisconlan Python Version: Current License: Non-SPDX

X-Ray Key Features Code Snippets Community Discussions(2)Vulnerabilities Install Support

kandi X-RAY | fast-python Summary

fast-python is a Python library. fast-python has no bugs, it has no vulnerabilities and it has low support. However fast-python build file is not available and it has a Non-SPDX License. You can download it from GitHub.

Source code for Fast Python (2020) by Chris Conlan. Paperback available for purchase on Amazon. The following code profiles can be run as stand-alone scripts. They may or may not depend on explanation provided in the accompanying book. Running them is simple ... All the profiles use a simple profiling module in src/utils/profiler.py. It produces tables and charts like the following. I use the profiler frequently in my own work. It allows me to analyze the relationship between computational complexity and raw execution time pretty easily. I have included a dependencies.txt, but you should be fine with a blank Python 3 environment followed by ...

Support

Quality

Security

License

Reuse

Support

fast-python has a low active ecosystem.

It has 109 star(s) with 20 fork(s). There are 5 watchers for this library.

It had no major release in the last 6 months.

There are 0 open issues and 1 have been closed. On average issues are closed in 2 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of fast-python is current.

Quality

fast-python has 0 bugs and 50 code smells.

Security

fast-python has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

fast-python code analysis shows 0 unresolved vulnerabilities.

There are 11 security hotspots that need review.

License

fast-python has a Non-SPDX License.

Non-SPDX licenses can be open source with a non SPDX compliant license, or non open source licenses, and you need to review them closely before use.

Reuse

fast-python releases are not available. You will need to build from source code and install.

fast-python has no build file. You will be need to create the build yourself to build the component from source.

Installation instructions are not available. Examples and code snippets are available.

fast-python saves you 1145 person hours of effort in developing the same functionality from scratch.

It has 2585 lines of code, 282 functions and 130 files.

It has high code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed fast-python and discovered the below as its top functions. This is intended to give you an instant insight into fast-python implemented functionality, and help decide if they suit your requirements.

Sort a list of values
Construct a max heap from values
Internal helper method for the heapify method
Compute the moving average of values
Wrapper around numpy filter
Return the list of words that match the two lists
Iterate through a Pandas DataFrame
Return a list of words that match the two lists
Multiply two lists
Count occurrences of the words in the list
Compute the moving moving average
Overriding best - case
Count the occurrence of a list of words
Given a list of values return a new list
Performs a fast count computation
Fast moving average
Compute a numpy ndarray with NaN
Compute moving average moving average
Sort a list
Apply the filter to a series
Sorts a list of values according to the smallest value
Helper function for numpy ndarray

Get all kandi verified functions for this library.

fast-python Key Features

No Key Features are available at this moment for fast-python.

fast-python Examples and Code Snippets

No Code Snippets are available at this moment for fast-python.

Community Discussions

Trending Discussions on fast-python

Fast Python algorithm for random partitioning with subset sums equal or close to given ratios

How do you use msgpack to replace copy.deepcopy in Python?

QUESTION

Fast Python algorithm for random partitioning with subset sums equal or close to given ratios

Asked 2021-Jun-12 at 15:14

This question is an extension of my previous question: Fast python algorithm to find all possible partitions from a list of numbers that has subset sums equal to a ratio . I want to divide a list of numbers so that the ratios of subset sums equal to given values. The difference is now I have a long list of 200 numbers so that a enumeration is infeasible. Note that although there are of course same numbers in the list, every number is distinguishable.

...

ANSWER

Answered 2021-Jun-12 at 15:14

You can use a greedy heuristic where you generate each partition from num_gen random permutations of the list. Each random permutation is partitioned into len(ratios) contiguous sublists. The fact that the partition subsets are sublists of a permutation make enforcing the ratio condition very easy to do during sublist generation: as soon as the sum of the sublist we are currently building reaches one of the ratios, we "complete" the sublist, add it to the partition and start creating a new sublist. We can do this in one pass through the entire permutation, giving us the following algorithm of time complexity O(num_gen * len(lst)).

Source https://stackoverflow.com/questions/67939449

QUESTION

How do you use msgpack to replace copy.deepcopy in Python?

Asked 2018-Jan-11 at 13:55

According to this post What is a fast pythonic way to deepcopy just data from a python dict or list ? msgpack is 10 times faster than copy.deepcopy but I cannot figure out how to use it.

I tried

...

ANSWER

Answered 2018-Jan-11 at 13:48

If you look at the docs, msgpack.unpack expects a stream, not a block of bytes:

Source https://stackoverflow.com/questions/48208507

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install fast-python

You can download it from GitHub.
You can use fast-python like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

Find more information at: