miku

*.pyc
.pytest_cache

miku

package main

import (
	"debug/elf"
	"fmt"
	"log"
	"os"

	"github.com/bnagy/gapstone"
)

miku

import shelve
import multiprocessing
import os

filename = "tmp.shelve"
N = 4
end = 10000


def insert((offset, jump, end, filename)): 

miku

package main

import (
	"bufio"
	"fmt"
	"net"
	"net/http"
	"net/url"
	"crypto/tls"

miku

import Tkinter
from Tkinter import Frame, BOTH, Canvas


#By Caleb Robinson
class Pong(Frame):
    player1 = 0
    player2 = 0
    ballX=50
    ballY=50

miku

I was drawn to programming, science, technology and science fiction
ever since I was a little kid. I can't say it's because I wanted to
make the world a better place. Not really. I was simply drawn to it
because I was drawn to it. Writing programs was fun. Figuring out how
nature works was fascinating. Science fiction felt like a grand
adventure.

Then I started a software company and poured every ounce of energy
into it. It failed. That hurt, but that part is ok. I made a lot of
mistakes and learned from them. This experience made me much, much

miku

miku

# One of the fastest ways to insert bulk data into Postgres (at least, aside from COPY) is using the psycopg2 extras function execute_values.
# However, this doesn't return an accurate row count value - instead, it just returns the row count for the last page inserted. 
# This wraps the execute_values function with its own pagination to return an accurate count of rows inserted. 
# Performance is approximately equivalent to underlying execute_values function - within 5-10% or so in my brief tests.

import psycopg2
import psycopg2.extras
import math

db_connection_string = "dbname=EDITME host=EDITME"

miku

package main

import (
	"database/sql"
	"database/sql/driver"
	"fmt"
	"net"
	"os"
	"time"

miku

#!/usr/bin/env python 
# Python 3

import datetime
import json
import os

from github import Github # pip install PyGithub

ORGANIZATIONS = ['my_organization', 'some_other_organization']