panicpotatoe

# -*- coding: utf-8 -*-
"""
Created on Mon Mar 11 13:49:40 2019

@author: Nhan Tran
"""

import numpy as np
import pandas as pd
import scipy.stats as stats

pplonski

# This is example, how to use MLJAR service for automatic machine learning and its R-wrapper for churn prediction.
# Example is based on data from https://github.com/WLOGSolutions/telco-customer-churn-in-r-and-h2o/tree/master/data
# Example by Dominik Krzemiński
library(mljar)
library(data.table)

# Read and clean the dataset
all_data <- fread("data/edw_cdr.csv")
all_data <- all_data[, !c("month", "year"), with = FALSE]
all_data <- all_data[complete.cases(all_data)]

ryanpraski

library(googleAnalyticsR)
library(tidyr)

#Authorized Google Analytics R- this will open a webpage
#You must be logged into your Google Analytics account on your web browser
ga_auth()

#Use the Google Analytics Management API to see a list of Google Analytics accounts you have access to
my_accounts <- google_analytics_account_list()
View(my_accounts)

wrobstory

cpard

# -*- coding: utf-8 -*-
"""
Created on Sun Jun  5 18:55:20 2016

@author: cpard
"""
from sqlalchemy import create_engine
import pandas as padas
import numpy as np
import statsmodels.api as sm

chipoglesby

SELECT
  distance, lat, long, temp
FROM
  (SELECT
    ((ACOS(SIN(39.73756700 * PI() / 180) *
           SIN((lat/1000) * PI() / 180) + 
           COS(39.73756700 * PI() / 180) *
           COS((lat/1000) * PI() / 180) * 
           COS((-104.98471790 -
           (long/1000)) * PI() / 180)) * 

kevindavenport

hrbrmstr

henryhund

 WITH 
    /*
    Find every visit
    */ 
    all_prospects_visits AS
  ( SELECT DISTINCT prospects.id AS prospect_id,
                    activity.created_at AS visit_date,
                    details AS visit_details,
                    activity.type AS visit_type,
                    activity.created_at created_at

elisebreda

CREATE OR REPLACE FUNCTION f_findall_emails( txt VARCHAR(20000) )
    RETURNS VARCHAR(20000) IMMUTABLE AS $$
    """Extract all email addresses found within a given string `txt`. 
    
    Return:
        A pipe-delimited string composed of any email addresses found. (e.g. 'john@example.com|jeane@example.com|sara@example.com')
    
    Example:

        mydb=#