megpay

i<-1

x1 <- data.frame(age=rep(tuyns$age[i], tuyns$controls[i]) , 
                 tobacco=rep(tuyns$tobacco[i], tuyns$controls[i]), 
                 alcohol=rep(tuyns$alcohol[i],  tuyns$controls[i]),
                 case.ctl=rep(0, tuyns$controls[i]))
for (i in 2:length(tuyns$id)) {
  new.df <- data.frame(age=rep(tuyns$age[i], tuyns$controls[i]) , 
                   tobacco=rep(tuyns$tobacco[i], tuyns$controls[i]), 
                   alcohol=rep(tuyns$alcohol[i],  tuyns$controls[i]),

megpay

# Connecting to a DB2 server using python.
import ibm_db

# Fill in credentials below.
dsn_driver = "{IBM DB2 ODBC DRIVER}"
dsn_database = "database"            # e.g. "EMPLOYEES"
dsn_hostname = "hostname"            # e.g.: "db2.myhost.com"
dsn_port = "port"                    # e.g. "50000" 
dsn_protocol = "protocol"            # i.e. "TCPIP"
dsn_uid = "username"                 # e.g. "myusername"

megpay

/* Create table syntax with an auto-incremented field. */
DROP TABLE NewEmployees;

/* Note the auto increment syntax. It is slightly different. */
CREATE TABLE NewEmployees (
		ID INT GENERATED BY DEFAULT AS IDENTITY 
		(START WITH 100 INCREMENT BY 10),
	first_name VARCHAR(50),
	last_name VARCHAR(50),
	salary INT, 

megpay

#!/bin/sh

# Read from the file file.txt and output the tenth line to stdout.
sed -n 10p file.txt

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/* 
Given 2 tables in a MySQL database, Employee and Department, find the employees (including ties) with the highest 
salaries for each department. 

Employee table: 
Id (auto incremented integer)
Name (Varchar, employee name)
DepartmentId (integer and key to Department table)
Salary (Integer)

megpay

# From this problem on leetcode. 

# https://leetcode.com/problems/average-salary-excluding-the-minimum-and-maximum-salary/

# Given an array of unique integers salary where salary[i] is the salary of the employee i.

# Return the average salary of employees excluding the minimum and maximum salary.

# Example: 
# Input: salary = [4000,3000,1000,2000]

megpay

megpay

#!/bin/sh 

# Where I am storing useful sed commands for later use. 

# Removing characters from a csv or text file. This removes a double-quote.
sed 's/"//g' filename.csv 
sed 's/"//g' filename.csv > filename_new.csv

# Not sed, but using cut to remove columns of a csv file.
# This takes a pipe-delimited file and removes columns 2, 3, and 4.

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/* Basic viewing the structure commands */
\c database_name --connect to a database.
\dt --view the tables. this filters out non-table relations.
\d  --view the tables. Includes non-table relations.
\d tablename --view the columns of the table.


/* Create a table and import from a csv into that table. */
CREATE TABLE employees (
  id SERIAL, 

megpay

# Manual garbage collection due to limited RAM - useful in Kaggle competitions
import sys
for i in dir(): 
    if sys.getsizeof(eval(i)) > 10000: 
        print("the size of {} is:".format(i), sys.getsizeof(eval(i))/1e+9)

import gc
del train   # delete a data frame
gc.collect()   # manual garbage collection