joahking

(a gist based on the old toolmantim article on setting up remote repos)

To collaborate in a distributed development process you’ll need to push code to remotely accessible repositories.

This is somewhat of a follow-up to the previous article setting up a new rails app with git.

For the impatient

Set up the new bare repo on the server:

timtadh

#!/usr/bin/env python
# Copyright (c) 2012, Tim Henderson
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are
# met:
#
# - Redistributions of source code must retain the above copyright notice,
#   this list of conditions and the following disclaimer.

pbugnion

This gist lets you keep IPython notebooks in git repositories. It tells git to ignore prompt numbers and program outputs when checking that a file has changed.

To use the script, follow the instructions given in the script's docstring.

For further details, read this blogpost.

The procedure outlined here is inspired by this answer on Stack Overflow.

dimitardanailov

# Path to your oh-my-zsh installation.
export ZSH=/Users/dimitar.danailov/.oh-my-zsh

# Set name of the theme to load.
# Look in ~/.oh-my-zsh/themes/
# Optionally, if you set this to "random", it'll load a random theme each
# time that oh-my-zsh is loaded.
# ZSH_THEME="robbyrussell"
ZSH_THEME="agnoster"

Swarchal

import pandas as pd
import numpy as np

def find_correlation(data, threshold=0.9, remove_negative=False):
    """
    Given a numeric pd.DataFrame, this will find highly correlated features,
    and return a list of features to remove.
    Parameters
    -----------
    data : pandas DataFrame

MichalZalecki

RUN ["apt-get", "update"]
RUN ["apt-get", "install", "-y", "zsh"]
RUN wget https://github.com/robbyrussell/oh-my-zsh/raw/master/tools/install.sh -O - | zsh || true

# docker exec -it my-app-container /bin/zsh

MatthieuBizien

# coding: utf-8

import logging
import re
from collections import Counter

import numpy as np
import torch
from sklearn.datasets import fetch_20newsgroups
from torch.autograd import Variable

rainsunny

Suppose you need to apply the same function to multiple columns in one DataFrame, one straight way is like this:

val newDF = oldDF.withColumn("colA", func("colA")).withColumn("colB", func("colB")).withColumn("colC", func("colC"))

If you want to save some type, you can try this:

1. Use select with varargs including *:

import spark.implicits._

ashokpant

#!/bin/bash

# install CUDA Toolkit v9.0
# instructions from https://developer.nvidia.com/cuda-downloads (linux -> x86_64 -> Ubuntu -> 16.04 -> deb)
CUDA_REPO_PKG="cuda-repo-ubuntu1604-9-0-local_9.0.176-1_amd64-deb"
wget https://developer.nvidia.com/compute/cuda/9.0/Prod/local_installers/${CUDA_REPO_PKG}
sudo dpkg -i ${CUDA_REPO_PKG}
sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/7fa2af80.pub
sudo apt-get update
sudo apt-get -y install cuda-9-0

weaming

#!/usr/bin/env python3
# coding: utf-8
"""
Author       : weaming
Created Time : 2018-05-26 21:32:59
Prerequisite:
    python3 -m pip install cson arrow
"""
import json
import os