Egor Borisov ehborisov

Vim cheat sheet

Starting Vim

vim [file1] [file2] ...

-#, --progress-bar Make curl display a simple progress bar instead of the more informational standard meter.

-b, --cookie <name=data> Supply cookie with request. If no =, then specifies the cookie file to use (see -c).

-c, --cookie-jar <file name> File to save response cookies to.

	"""
	The schemas that Spark produces for DataFrames are typically
	nested, and these nested schemas are quite difficult to work with
	interactively. In many cases, it's possible to flatten a schema
	into a single level of column names.
	"""

	import typing as T

	import cytoolz.curried as tz

	# A simple cheat sheet of Spark Dataframe syntax
	# Current for Spark 1.6.1

	# import statements
	#from pyspark.sql import SQLContext
	#from pyspark.sql.types import *
	#from pyspark.sql.functions import *
	from pyspark.sql import functions as F

	#SparkContext available as sc, HiveContext available as sqlContext.

	"""
	Convert Pandas DFs in an HDFStore to parquet files for better compatibility
	with Spark.

	Run from the command line with:

	spark-submit --driver-memory 4g --master 'local[*]' hdf5_to_parquet.py

	"""
	import pandas as pd

	#!/bin/bash
	#####################################################
	# Name: Bash CheatSheet for Mac OSX
	#
	# A little overlook of the Bash basics
	#
	# Usage:
	#
	# Author: J. Le Coupanec
	# Date: 2014/11/04

	class BSTnode(object):
	"""
	Representation of a node in a binary search tree.
	Has a left child, right child, and key value, and stores its subtree size.
	"""
	def __init__(self, parent, t):
	"""Create a new leaf with key t."""
	self.key = t
	self.parent = parent
	self.left = None

	from collections import defaultdict
	from heapq import *

	def dijkstra(edges, f, t):
	g = defaultdict(list)
	for l,r,c in edges:
	g[l].append((c,r))

	q, seen, mins = [(0,f,())], set(), {f: 0}
	while q:

	import sys

	from pyspark.context import SparkContext
	from numpy import array, random as np_random
	from sklearn import linear_model as lm
	from sklearn.base import copy

	N = 10000 # Number of data points
	D = 10 # Numer of dimensions
	ITERATIONS = 5