chat_block_1.py

import requests
import json
from itertools import zip_longest
import pandas as pd
import numpy as np

chat_block_10.py

#create the dataframe, list comprehension creates an array of rows based on the column names we got
#Let pandas do the hard part with csvs
csv_frame = pd.DataFrame([[i[j] for j in col_keys] for i in all_rows_as_obj], columns=col_keys)

chat_block_11.py

#coerce ticket_id to be int not float
csv_frame['ticket_id'] = csv_frame['ticket_id'].fillna(0).astype(np.int64)

#fill blank values with the string 'N/A'
csv_frame = csv_frame.fillna("N/A")

#output to a file
csv_frame.to_csv('october_stats.csv')

chat_block_2.py

config = {
  "username": "EMAILGOESHERE",
  "password": "PASSWORDGOESHERE",
  "base_path": "https://www.zopim.com/api/v2"
}

chat_block_3.py

#Since we are hitting the same subdomain with same endpoints lets take care of headers and auth just one time and reuse
s = requests.Session()
s.auth = (config['username'], config['password'])
s.headers.update({"content-type":"application/json"})

chat_block_4.py

#This is a little helper funciton that will take x of something, and let you chunk through a subset
#Note that 'n' is the chunk size
def _grouper(n, iterable, fillvalue=None):
    args = [iter(iterable)] * n
    return zip_longest(fillvalue=fillvalue, *args)

chat_block_5.py

#Since you want a report by month, lets just create a function to get all the chat IDs for a two digit month and 4 digit year
def get_chat_ids_for_month(year, month):
  all_chat_ids = []
  search_query = "timestamp:[{0}-{1}-01 TO {0}-{1}-31]".format(year, month)
  params = {"q":search_query}
  url = "{0}/chats/search".format(config['base_path'])
  while url:
    r = s.get(url, params=params)
    if r.status_code == 200:
      response = json.loads(r.text)
      for chat in response['results']:
        all_chat_ids.append(chat['id'])
        url = response['next_url']
    else:
      print("A request failed in the loop")
  return all_chat_ids

chat_block_6.py

def build_csv_data(queried_id_list):
  all_chats_for_csv = []
  for chunk in _grouper(50, queried_id_list):
    filtered = [id for id in chunk if id is not None] #Only needed for last set of 50 that will have some sort of remainder filled with 'None' values by the grouper() function
    url = "{0}/chats".format(config['base_path'])
    stringified_ids = ",".join(filtered)
    params = {"ids":stringified_ids}
    r = s.get(url, params=params)
    if r.status_code == 200:
      response = json.loads(r.text)
      docs = response['docs']
      for k in docs:
        doc_obj = docs[k] #This is the 'doc' object. Note that some keys may not exist for all items. Duration does not exist for offline messages for example
        all_chats_for_csv.append(doc_obj)
    else:
      print("error getting bulk chats")
  return all_chats_for_csv

chat_block_7.py

#grab the ids for the specified range NOTE!! This is where you can change the year/month for the date range.
queried_id_list = get_chat_ids_for_month(2016, 10)

#turn those into raw objects
csv_obj_data = build_csv_data(queried_id_list)

chat_block_8.py

#create a list to hold our rows
all_rows_as_obj = []

#turn the raw objects into nice and tasty ones that pandas can digest
for record in csv_obj_data:
  try:
    response_time = record.get('response_time', {})
    count = record.get('count',{})
    csv_obj = {
      "id": record['id'],
      "agent": ",".join(record.get('agent_names', "N/A")),
      "visitor": record['visitor']['name'],
      "department": record['department_name'],
      "url": record.get('webpath', "N/A"),
      "missed": record.get('missed', "N/A"),
      "resp_first": response_time.get('first', []),
      "resp_max": response_time.get('max', []),
      "resp_avg": response_time.get('avg', []),
      "start timestamp": record['session']['start_date'],
      "end timestamp": record['session']['end_date'],
      "total messages": count.get('total', 0),
      "Agent Msg Count": count.get('agent', 0),
      "Visitor Msg Count": count.get('visitor', 0),
      "rating": record.get('rating',[]),
      "ticket_id": record['zendesk_ticket_id']
    }
  except KeyError as e:
    print(e)
    print("key error occured for record with id: {}".format(record['id']))
    print(record)
    pass
  all_rows_as_obj.append(csv_obj)

chat_block_9.py

#with prettier objects our column names can just come from a call to the first object's keys
col_keys = all_rows_as_obj[0].keys()