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# Client
from multiprocessing.connection import Client

address = ('localhost', 6000)
conn = Client(address)
conn.send('test')

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from socket import *

s_socket = socket(AF_INET, SOCK_STREAM)
s_socket.connect(("localhost", 6000))
while (True):
	s_socket.send('Test\n'.encode())

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from selenium import webdriver
 
#browser = webdriver.Firefox()
browser = webdriver.Chrome()
 
browser.get("http://movie.naver.com/movie/bi/pi/filmo.nhn?code=71351")  
browser.switch_to.frame("filmoContentsIframe");

browser.find_elements_by_css_selector('#filmoOrder')[0].click()
menus = browser.find_elements_by_css_selector('#filmoOrderLayer ul li a')

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// ReadBMjuP.cpp : Defines the entry point for the console application.
//

#include <iostream>
#include <vector>
#include <math.h>
#include <stdlib.h>
#include <time.h>
#include <algorithm>
#include <list>

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package main

import (
    "fmt"
)

type result struct {
    src_text  string
    result_sz string
    is_done   bool

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import operator

# https://docs.python.org/3/library/operator.html
myOp = operator.and_
print bool( myOp(myOp(3==3,1==1), myOp(2==2, 1==1)) )

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import collections.abc

class CustomData():
    def __init__(self, iterable):
        self.value = []
        for value in iterable:
            self.value.append(value)
    def __eq__(self, value):
        ret = []
        for v in self.value:

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import datetime as dt
today = dt.datetime.now()
days_in_the_year = (dt.date(today.year, today.month, today.day) - dt.date(today.year,1,1)).days + 1
print(days_in_the_year)

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from collections import OrderedDict, Counter
import pprint

def main():
    dict_data = {
        'list_1':[1, 2, 3, 4, 5, 6],     
        'list_2':[1, 5, 6, 7, 8, 9],     
        'list_3':[10, 11, 12, 13, 14, 15], 
        'list_4':[132, 14, 134, 135, 136, 137], 
        'list_5':[232, 214, 224, 235, 236, 237],

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import pandas as pd
import numpy as np
from sqlalchemy import create_engine

engine = create_engine('sqlite:///:memory:')
n=9999
df = pd.DataFrame(np.random.randn(n, 2))
df.columns = ['a','b']
df = df.astype('float32')
df.to_sql('data_chunked', con=engine, chunksize=(499), index=None)