yong27

import multiprocessing
import pandas as pd
import numpy as np

def _apply_df(args):
    df, func, kwargs = args
    return df.apply(func, **kwargs)

def apply_by_multiprocessing(df, func, **kwargs):
    workers = kwargs.pop('workers')

Zearin

NOTE: This is a question I found on StackOverflow which I’ve archived here, because the answer is so effing phenomenal.

---

Q: How can I make a chain of function decorators in Python?

---

If you are not into long explanations, see [Paolo Bergantino’s answer][2].

amlang

# PHP / Ruby
# Regex find key and value pair in JSON formated string
# Match 1: Key
# Match 2: Value
# https://regex101.com/r/zR2vU9/4
# http://rubular.com/r/KpF3suIL10
# http://stackoverflow.com/questions/14349889/how-to-use-a-regular-expression-to-extract-json-fields/35129815#35129815

(?:\"|\')(?<key>[^"]*)(?:\"|\')(?=:)(?:\:\s*)(?:\"|\')?(?<value>true|false|[0-9a-zA-Z\+\-\,\.\$]*)

bastman

Docker - How to cleanup (unused) resources

Once in a while, you may need to cleanup resources (containers, volumes, images, networks) ...

delete volumes

// see: https://github.com/chadoe/docker-cleanup-volumes

$ docker volume rm $(docker volume ls -qf dangling=true)

$ docker volume ls -qf dangling=true | xargs -r docker volume rm

blaylockbk

import multiprocessing
from multiprocessing.dummy import Pool as ThreadPool

import numpy as np


def my_multipro(items, func, max_cpus=12):
    """Do an embarrassingly parallel task using multiprocessing.

    Use this for CPU bound tasks.

tejaslodaya

import multiprocessing
import pandas as pd
import numpy as np

def _apply_df(args):
    df, func, num, kwargs = args
    return num, df.apply(func, **kwargs)

def apply_by_multiprocessing(df,func,**kwargs):
  workers=kwargs.pop('workers')

carlthome

NegatioN

def _to_one_hot(y, num_classes):
    scatter_dim = len(y.size())
    y_tensor = y.view(*y.size(), -1)
    zeros = torch.zeros(*y.size(), num_classes, dtype=y.dtype)
        
    return zeros.scatter(scatter_dim, y_tensor, 1)


print(_to_one_hot(torch.as_tensor([2, 4, 7]), num_classes=10))
print(_to_one_hot(torch.as_tensor([[1, 5 ,6], [2, 4, 7]]), num_classes=10))

bogdan-kulynych

# WARNING: These steps seem to not work anymore!

#!/bin/bash

# Purge existign CUDA first
sudo apt --purge remove "cublas*" "cuda*"
sudo apt --purge remove "nvidia*"

# Install CUDA Toolkit 10
wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/cuda-repo-ubuntu1804_10.0.130-1_amd64.deb

toddbirchard

"""Extract nested values from a JSON tree."""


def json_extract(obj, key):
    """Recursively fetch values from nested JSON."""
    arr = []

    def extract(obj, arr, key):
        """Recursively search for values of key in JSON tree."""
        if isinstance(obj, dict):