miltonlab

import decimal
from decimal import Decimal
import datetime

#Read from Microsoft Access
#rows = cursor_access.execute("SELECT * FROM Predios")
#One record example:
row = (98, '11', '04', '57', '01', '01', '004', '005', '000', '00', '000', '1104570101004005', 'EDIFICADO', 'TNTE MAXIMILIANO RODRIGUEZ', 'UNIPROPIEDAD', 'Residencial', 'C:\\FOTOS\\570101\\004\\005.jpg', 'MODESTO BURNEO', 'S/N', 'JACINTA REYES', 'LOJA, SAN SEBASTIAN', 'Natural', None, datetime.datetime(1973, 1, 26, 0, 0), 44, 'Femenino', False, 'CARMEN PAULINA', 'ILLESCAS BURNEO', 'Cédula', '1102962741', '-', '-', 'Casado', 'MANUEL AGUSTIN', 'TOLEDO CUEVA', 'Cédula', '-', '-', '-', 'BURNEO CAMPOVERDE JULIO MARIANA', '-', None, None, None, None, None, None, None, 'Cédula', None, None, None, False, True, 'Loja', 'Celica', '3', datetime.datetime(2011, 10, 7, 0, 0), '254', datetime.datetime(2011, 10, 31, 0, 0), False, 0, 0, 'Compra - venta', True, 'Calle', 'Adoquín de cemento', True, True, True, True, False, True, True, True, False, True, False, False,

miltonlab

# List branches for remote repositorie
git ls-remote --heads https://github.com/jquemada/cal_branches

miltonlab

import urllib.request
import json

def analisa_detalhe(cod):
  url = 'http://educacao.dadosabertosbr.com/api/escola/'
  resp = urllib.request.urlopen(url+str(cod)).read()
  resp = json.loads(resp.decode('utf-8'))
  if int(resp['salasExistentes']) > 1:
    print ('Salas Existentes:', resp['salasExistentes'])
    print ('Funcionários:', resp['funcionarios'])

miltonlab

from  numpy import array
a = array([[5.1, 7.4, 3.2, 9.9],
           [1.9, 6.8, 4.1, 2.3],
           [2.9, 6.4, 4.3, 1.4]])
#cantidad de elemtos
a.size
#dimensiones
a.shape
#Lectura: Obtener un elemento contenido en la matriz
a[1, 3]

miltonlab

edades = numpy.array[(20,23,22)]
#lectura
print (edades[2])
#escritura
edades[1] = 27
#presentacion
for i in range(0, len(edades)):
  print (edades[i])
#aunque en python no hace falta el bucle
print(edades)

miltonlab

import numpy
#creacion e inicializacion
a = numpy.array([2,4,5,9])
#creacion
b = numpy.array()
#inicializacion, por defecto el tipo de dato usado es float
b = numpy.empty(4)
#Mas formas de inicializacion
dias = numpy.empty(7)
edades = numpy.empty(20, dtype=int)

miltonlab

#!/usr/bin/env python

"""
=====================================
PEP 20 (The Zen of Python) by example
=====================================

Usage: %prog

:Author: Hunter Blanks, hblanks@artifex.org / hblanks@monetate.com

miltonlab

echo "" > /tmp/bads.txt
inicio=164
fin=165
for i in `seq $inicio $fin`
do 
  ip=10.10.$i.1
  if ping $ip -w2 | grep "0 received"
  then 
  echo $ip >> /tmp/bads.txt
  fi

miltonlab

## dplyr package notes
# dplyr use plyr and haver relation with data.table
library(dplyr)
# Example
ddply(df, ~date, summarise, totalSteps=sum(steps))
## *Complete dni ecuatorian numbers with '0'. If length == 9
## Sometimes usefull when Spreadsheet cut that number at start
 
# =========================================================================================
> head(reporte)

miltonlab

# ---------------------------------------------------
# Update LinuxMint to v17
# ---------------------------------------------------
sudo sed -i 's/raring/trusty/' /etc/apt/sources.list
sudo sed -i 's/olivia/qiana/' /etc/apt/sources.list
sudo sed -i 's/raring/trusty/' /etc/apt/sources.list.d/official-package-repositories.list
sudo sed -i 's/olivia/qiana/' /etc/apt/sources.list.d/official-package-repositories.list
sudo apt-get update
sudo apt-get dist-upgrade
sudo apt-get upgrade