anhdanggit

## Install this in terminal: !pip install --upgrade google-api-python-client

## This func to call googleapi, apply the face_detection on img, and output the happy/unhappy likelihood
def detect_joy(IMG):
  from googleapiclient.discovery import build
  vservice = build('vision', 'v1', developerKey=APIKEY)
  ## CHECK THIS: https://cloud.google.com/vision/docs/reference/rest/v1/images/annotate
  ## The have the interactive tool to create the body of request
  request = vservice.images().annotate(body={
    "requests": [

anhdanggit

## Import modules
import pandas as pd
import numpy as np
import scipy as sp
import pandas_datareader.data as web
from datetime import datetime as dt ## datetime
import json ## json
import requests ## API
from lxml import objectify
import os 

anhdanggit

copy_to_clipboard = function(x,sep="\t",col.names=T,...) { 
  write.table(x
              ,file = pipe("pbcopy")
              ,sep=sep
              ,col.names = col.names
              ,row.names = F
              ,quote = F,...)
}

anhdanggit

## Comprehensive -----
strings = ['a', 'as', 'bat', 'car', 'dove', 'python']
[x.upper() for x in strings if len(x) > 2] ## list comprehensive
{x.upper() for x in strings if len(x) > 2} ## set comprehensive
{key: val.upper() for key,val in enumerate(strings)} ## dict comprehensive

## Nested Comprehensive ----
all_data = [['John', 'Emily', 'Michael', 'Mary', 'Steven'],
            ['Maria', 'Juan', 'Javier', 'Natalia', 'Pilar', 'Eerin']]
## full

anhdanggit

## Functions -----------

## Multiple return
def f():
    a = 5
    b = 6
    c = 7
    return a, b, c

a, b, c = f()

anhdanggit

# Categorizing to dicts
words = ['apple', 'bat', 'bar', 'atom', 'book']
by_letters = {}

for word in words:
    letter = word[0]
    by_letters[letter] = by_letters.get(letter, 0) + 1
    value = by_letters[letter]
    print('Letter: {} - Count: {}'.format(letter, value))

anhdanggit

## Tuple: 
# Capture arbitrarily long list
values = 1,2,3,4,5
a, b, *rest = values
# Discard the rest
a, b, *_ = values

## Zip: 
# "pairs" up the elements of list
seq1 = ['foo','bar','baz']

anhdanggit

data_xml <- dbGetQuery(conn = DB_conn,statement = qq('

                    SELECT decision_id, data,
                                         xpath(\'//application-uuid\',data) as application_uuid,
                                         xpath(\'//brokerage-id\',data) as brokerage_id,
                                         xpath(\'//Transformed\',data) as transformed,
                                         xpath(\'//pd_commercial\',data) as pd_commercial,

                                         xpath(\'//row_pd_comb\',data) as row_pd_comb,
                                         xpath(\'//marginal_group\',data) as marginal_group,

anhdanggit

## Code to extract all fields from json
sphonic_data <- tbl(connection_risk_house(), sql(
    "SELECT distinct on (application_id,jkey)
    application_id,
    jsonb_object_keys(section_payload) as jkey,
    jsonb_object_field_text(section_payload, jsonb_object_keys(section_payload)) as jvalue
    FROM fraud_detection.response_sections
    WHERE  sent_at >= '2017-10-01'
    AND sent_at < '2017-12-01'
    GROUP BY application_id,section_payload

anhdanggit

library(DBI)
library(dplyr)
library(GetoptLong)
library(magrittr)

data_warehouse <- function(){DBI::dbConnect(RPostgres::Postgres()
                                               ,dbname="database"
                                            ## to hide the credentials (set it up in .Renv)
                                               ,host=Sys.getenv("HOST")
                                               ,user= Sys.getenv("USERNAME")