data_sources_api/customer_read_api.py at master · dsamanuel/data_sources_api · GitHub

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import requests
from datetime import datetime
from requests.auth import HTTPBasicAuth
import json
import pandas as pd

from itertools import chain

import os
from dotenv import load_dotenv

load_dotenv()
base_url = os.getenv("base_url")+"customers"

#djflk
# Build paths inside the project like this: BASE_DIR / 'subdir'.


 # This loads variables from .env into os.environ


def get_all_time_entries():
    base_url = os.getenv("base_url")+"customers"


    headers = { 'Content-Type':"application/json",

         'X-API-KEY': os.getenv("X_API_KEY"),
            'X-API-SECRET': os.getenv("X_API_SECRET"),
            "authentication_token" : os.getenv("authentication_token"),


           }


    querystring = {'customer_only': True }


    # find out total number of pages
    res = res = requests.get(url=base_url,headers=headers,params=querystring).json()
    #total_pages = int(r['info']['pages'])

    cursor = res['cursor']
    # results will be appended to this list
    all_time_entries = res['data']
    page = 1


    # loop through all pages and return JSON object
    while True:
        base_url = url =  os.getenv("base_url")+"customers?cursor="+str(cursor)


        headers = { 'Content-Type':"application/json",
           'X-API-KEY': os.getenv("X_API_KEY"),
            'X-API-SECRET': os.getenv("X_API_SECRET"),
            "authentication_token" : os.getenv("authentication_token"),
           }


        res = requests.get(url=base_url, headers=headers,params=querystring)
        #print(page)
        resj = res.json()
        cursor = resj['cursor']


        list_of_dicts = resj['data']
        d = len(list_of_dicts)

        for i in range(1, d):
           all_time_entries.append(list_of_dicts[i])


        if not cursor:
            break
        page = page + 1

    # prettify JSON
    #datas = json.dumps(all_time_entries, sort_keys=True, indent=4)

    return all_time_entries

data = get_all_time_entries()


customers_df = pd.DataFrame(columns=['name', 'code', 'phone_number','id','serial','last_plan_renewal','next_plan_renewal','service_area_id','site_id','last_energy_limit_reset_at','last_energy_limit_reset_energy','energy_limited','balance_credit_value','balance_plan_value','balance_technical_debt_value'])

def lat(coord):
    if coord is None:
        lat = None
    else:
        lat = coord['latitude']

    return lat
def long(coord):
    if coord is None:
        long = None
    else:
        long = coord['longitude']

    return long

for i in range(0, len(data)):
    print(data[i])
    if data[i]['meters']:
        datac = {'name':data[i]['name'], 'code':data[i]['code'], 'phone_number':data[i]['phone_number'],'id':data[i]['id'],'serial':data[i]['meters'][0]['serial'],'last_plan_renewal':data[i]['last_plan_renewal'],'next_plan_renewal':data[i]['next_plan_renewal'],'service_area_id':data[i]['service_area_id'],'site_id':data[i]['site_id'], 'last_energy_limit_reset_at':data[i]['last_energy_limit_reset_at'], 'last_energy_limit_reset_energy':data[i]['last_energy_limit_reset_energy'], 'energy_limited':data[i]['energy_limited'],'balance_credit_value':data[i]['balances']['credit']['value'],'balance_plan_value':data[i]['balances']['plan']['value'],'balance_technical_debt_value':data[i]['balances']['technical_debt']['value']}
    else:
        datac = {'name':data[i]['name'], 'code':data[i]['code'], 'phone_number':data[i]['phone_number'],'id':data[i]['id'],'last_plan_renewal':data[i]['last_plan_renewal'],'next_plan_renewal':data[i]['next_plan_renewal'],'service_area_id':data[i]['service_area_id'],'site_id':data[i]['site_id'], 'last_energy_limit_reset_at':data[i]['last_energy_limit_reset_at'], 'last_energy_limit_reset_energy':data[i]['last_energy_limit_reset_energy'], 'energy_limited':data[i]['energy_limited'],'balance_credit_value':data[i]['balances']['credit']['value'],'balance_plan_value':data[i]['balances']['plan']['value'],'balance_technical_debt_value':data[i]['balances']['technical_debt']['value']}

    cdf = pd.DataFrame(datac, index=[0])
    customers_df = pd.concat([customers_df, cdf], ignore_index=True)


#customer_df = customers_df[customers_df[['serial']].notnull().all(1)]
#customer_df = customer_df.drop_duplicates()
#customer_df= customer_df.reset_index(drop=True)
customers_df.to_csv('customer_final_df.csv', index=False)