#####

# Data Buffet API

# Code sample: Python

# 1 December 2017

# (c)2017 Moody's Analytics

import requests

import hashlib

import hmac

import datetime

import json

import pandas as pd

from time import sleep

from io import BytesIO

import binascii

#####

# Function: Make API request, including a freshly generated signature.

#

# Arguments:

# 1. Part of the endpoint, i.e., the URL after "https://api.economy.com/data/v1/"

# 2. Your access key.

# 3. Your personal encryption key.

# 4. Optional: default GET, but specify POST when requesting action from the API.

#

# Returns:

# HTTP response object.

def api_call(apiCommand, accKey, encKey, call_type="GET"):

url = "https://api.economy.com/data/v1/" + apiCommand

timeStamp = datetime.datetime.strftime(

datetime.datetime.utcnow(), "%Y-%m-%dT%H:%M:%SZ")

payload = bytes(accKey + timeStamp, "utf-8")

signature = hmac.new(bytes(encKey, "utf-8"), payload, digestmod=hashlib.sha256)

head = {"AccessKeyId":accKey,

"Signature":signature.hexdigest(),

"TimeStamp":timeStamp}

sleep(1)

if call_type == "POST":

response = requests.post(url, headers=head)

elif call_type =="DELETE":

response = requests.delete(url, headers=head)

else:

response = requests.get(url, headers=head)

return(response)

#####

# Setup:

# 1. Store your access key, encryption key, and basket name.

# Get your keys at:

# https://www.economy.com/myeconomy/api-key-info

ACC_KEY = "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX"

ENC_KEY = "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX"

BASKET_NAME = "TEST BASKET NAME"

#####

# Identify a basket to execute:

# 2. Get list of baskets.

# 3. Extract the basket with a given name, and save its ID for later.

baskets = pd.DataFrame(json.loads(api_call("baskets/", ACC_KEY, ENC_KEY).text))

basketId = baskets.loc[baskets["name"]==BASKET_NAME, "basketId"].item()

print("Basket ID: " + basketId)

print("Basket Name: " + BASKET_NAME)

# 4. Execute a particular basket using its ID.

# This requires that the optional argument "type" be set to "POST".

call = ("orders?type=baskets&action=run&id=" + basketId)

order = api_call(call, ACC_KEY, ENC_KEY, call_type="POST")

orderId = order.text[12:48]

print("Order ID: " + orderId)

#####

# Download the output:

# 5. Periodically check if the order has completed.

call = "orders/" + orderId

processing_check = True

while processing_check:

sleep(5)

status = api_call(call, ACC_KEY, ENC_KEY)

processing_check = json.loads(status.content.decode('utf-8'))['processing']

print('processing: ' + str(processing_check))

# 6. Download completed output.

new_call = ("orders?type=baskets&id=" + basketId)

get_basket = api_call(new_call, ACC_KEY, ENC_KEY)

## Choose one from below two line of codes:

get_basket = (str(get_basket.content).split("\\r\\n")) ## This line of code for csv files

get_basket = pd.read_excel(BytesIO(get_basket.content)) ## This line of code for xlsx files

# 7. Format the data frame.

data_df= pd.DataFrame(get_basket)

data_df = data_df[0].str.split(',', expand=True)

headers = data_df.iloc[0]

data_df.dropna(axis=1, how='all')

filter = data_df != ""

data_df = data_df[filter]

# 8. Summary of the data frame.

num_rows = str(len(data_df.index))

num_columns = str(len(data_df.columns))

print("Ready to use "+ BASKET_NAME + " DataFrame!")

print("DataFrame contains: " + num_columns + " columns & " + num_rows + " rows")