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pursh2002 / .py
Created January 11, 2018 14:07 — forked from linux-02/.py
Coordinates Calculator
from math import sqrt as sq
class Line(object):
def __init__(self, coor1=(0,0), coor2=(0,0)):
self.coor1 = coor1
self.coor2 = coor2
#THIS DEF DISTANCE TAKES THE VALUE OF COOR1 and COOR2 AND
#SOLVE FOR THE DISTANCE OF THE 2 GIVEN POINTS AND PRINT A STATEMENT
# 10_basic.py
# 15_make_soup.py
# 20_search.py
# 25_navigation.py
# 30_edit.py
# 40_encoding.py
# 50_parse_only_part.py
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@pursh2002
pursh2002 / 0-startup-overview.md
Created March 17, 2017 18:42 — forked from dideler/0-startup-overview.md
Startup Engineering notes
@pursh2002
pursh2002 / chap3.md
Created November 18, 2015 02:05 — forked from dapangmao/chap3.md

Chapter 3. Programming with RDDs

This chapter introduces Spark’s core abstraction for working with data, the Resilient Distributed Dataset (RDD). An RDD is simply a distributed collection of elements. In Spark all work is expressed as either creating new RDDs, transforming existing RDDs, or calling operations on RDDs to compute a result. Under the hood, Spark automatically distributes the data contained in RDDs across your cluster and parallelizes the operations you perform on them.

Both Data Scientists and Engineers should read this chapter, as RDDs are the core concept in Spark. We highly recommend that you try some of these examples in an interactive shell (see Introduction to Spark’s Python and Scala Shells). In addition, all code in this chapter is available in the book’s GitHub repository.

@pursh2002
pursh2002 / mod.py
Created November 17, 2015 02:32 — forked from anonymous/mod.py
while estado==1:
opcion=input('Elija una opcion del menu ')
if opcion==1:
neto=0.0
pi=shelve.open("db_productos.dat")
while code_bar!=0:
h=0
code_bar=input('Digite el codigo de barras o presione 0 para terminar su cuenta ')
if code_bar!=0:
@pursh2002
pursh2002 / Slice1.py
Last active August 29, 2015 14:08 — forked from atvKumar/Slice1.py
# In[5]:
x = [[ 795. , 501.0292887 ],
[ 794.97154472, 501. ],
[ 794.96078431, 500. ],
[ 795. , 499.09090909],
[ 795.03921569, 500. ],
[ 795.02777778, 501. ],
[ 795. , 501.0292887 ]]