def calls_by_csr():
call_file = p.parse(CALL_DATA, ",")
begin_date = "August 1, 2014"
end_date = "August 31, 2014"
counter = Counter(
item["Agent"] for item in call_file
if item["Date/Time"] >= begin_date and item["Date/Time"] <= end_date
and item["Call Status"] == "Completed" and item["Minutes"] > "1")
call_list = [
counter["Brent Gauthier"], counter["Joey Doughty"],
counter["Matt Intemann"], counter["Branton Phillips"],
counter["Matt Averyhart"], counter["Jacob Ellis"],
counter["Charles Marczynski"]
]
csr_list = tuple(
["BJ", "Joey", "Iceman", "Branton", "Averyhart", "Jacob", "Chuck"])
xlocations = na.array(range(len(csr_list))) + 0.5
width = 0.5
rects1 = plt.bar(xlocations, call_list, width, color='green')
plt.xticks(xlocations + width / 2, csr_list, rotation=90)
plt.subplots_adjust(bottom=0.2)
plt.rcParams['figure.figsize'] = 12, 12
plt.suptitle("Inbound & Outboud Calls Per Rep From " + begin_date +
" to " + end_date,
fontsize=12)
plt.ylabel("Number of Inbound & Outbound Calls > 1 min", fontsize=12)
autolabel(rects1)
plt.savefig("phone_calls_graph.png")
plt.clf()
def visualize_days():
"""Visualize data by day of week"""
#grab our parsed data tha we parsed earlier
data_file = prs.parse(MY_FILE, ",")
#make a new variable, 'counter', from iterating through each
#line of data in the parsed data, and count how many incidents
#happen on each day of the week
counter = Counter(item["DayOfWeek"]
for item in data_file) #for item in data_file:
#separate the x-axis data (the days of the week) from the #return counter
#'counter' variable from the y-axis data (the number of
#incidents for each day)
data_list = [
counter["Monday"], counter["Tuesday"], counter["Wednesday"],
counter["Thursday"], counter["Friday"], counter["Saturday"],
counter["Sunday"]
]
day_tuple = tuple(["Mon", "Tues", "Wed", "Thurs", "Fri", "Sat", "Sun"])
# with that y-axis data, assign it tho a matplotlib plot instance
plt.plot(data_list)
#create the amount of ticks needed for our x-axis, and assign
#the labels
plt.xticks(range(len(day_tuple)), day_tuple)
#save the plot
plt.savefig("Days.png")
#close plot file
plt.clf()
def booked_moves_by_csr():
order_file = p.parse(BELLHOP_ORDER, ",")
begin_date = raw_input("Start date: ")
end_date = raw_input("End date: ")
counter = Counter(
item["booked_by"] for item in order_file
if item["booked_at_date"] >= begin_date
and item["booked_at_date"] <= end_date and item["deposit_paid"] != "")
booked_list = [
counter["*****@*****.**"],
counter["*****@*****.**"]
]
csr_list = tuple(["Averyhart", "Graves"])
xlocations = na.array(range(len(csr_list))) + 0.5
width = 0.5
rects1 = plt.bar(xlocations, booked_list, width, color='green')
plt.xticks(xlocations + width / 2, csr_list, rotation=90)
plt.subplots_adjust(bottom=0.2)
plt.rcParams['figure.figsize'] = 12, 12
plt.suptitle("Booked Moves from " + begin_date + " to " + end_date,
fontsize=12)
plt.ylabel("Moves Booked", fontsize=12)
autolabel(rects1)
plt.savefig("manager_booked_moves_bar_graph.png")
plt.clf()
def create_contested_order_list():
contest_file = p.parse(TICKET_FILE, ",")
contested_orders = []
for ticket in contest_file:
split_sub = ticket["Subject"].split()
if range(len(split_sub)) > range(
0, 3) and ticket["Created at"] > "2014-10-01 08:00":
if split_sub[0] == 'Contested:':
contested_orders.append(split_sub[1])
elif split_sub[3] == 'contesting':
contested_orders.append(split_sub[7])
return contested_orders
def moves_per_hour_worked_by_csr():
order_file = p.parse(BELLHOP_ORDER, ",")
begin_date = raw_input("Start date: ")
end_date = raw_input("End date: ")
ice_hours = raw_input("Ice Hours: ")
joey_hours = raw_input("Joey Hours: ")
bj_hours = raw_input("BJ Hours: ")
chuck_hours = raw_input("Chuck Hours: ")
jacob_hours = raw_input("Jacob Hours: ")
justin_hours = raw_input("Justin Hours: ")
tisha_hours = raw_input("Tisha Hours: ")
olivia_hours = raw_input("Olivia Hours: ")
aaron_hours = raw_input("Aaron Hours: ")
stacy_hours = raw_input("Stacy Hours: ")
counter = Counter(
item["booked_by"] for item in order_file
if item["booked_at_date"] >= begin_date
and item["booked_at_date"] <= end_date and item["deposit_paid"] != "")
booked_list = [
(float(counter["*****@*****.**"]) / float(ice_hours)),
(float(counter["*****@*****.**"]) / float(joey_hours)),
(float(counter["*****@*****.**"]) / float(bj_hours)),
(float(counter["*****@*****.**"]) /
float(chuck_hours)),
(float(counter["*****@*****.**"]) / float(jacob_hours)),
(float(counter["*****@*****.**"]) / float(justin_hours)),
(float(counter["*****@*****.**"]) /
float(tisha_hours)),
(float(counter["*****@*****.**"]) / float(olivia_hours)),
(float(counter["*****@*****.**"]) /
float(aaron_hours)),
(float(counter["*****@*****.**"]) / float(stacy_hours))
]
csr_list = tuple([
"Iceman", "Joey", "BJ", "Chuck", "Jacob", "Justin", "Tisha", "Olivia",
"Aaron", "Stacy"
])
xlocations = na.array(range(len(csr_list))) + 0.5
width = 0.5
rects1 = plt.bar(xlocations, booked_list, width, color='green')
plt.xticks(xlocations + width / 2, csr_list, rotation=90)
plt.subplots_adjust(bottom=0.2)
plt.rcParams['figure.figsize'] = 12, 12
plt.suptitle("Booked moves per hour worked from " + begin_date + " to " +
end_date,
fontsize=12)
plt.ylabel("Booked Moves Per Hour Worked", fontsize=12)
autolabel(rects1)
plt.savefig("moves_per_hour_worked_by_csr.png")
plt.clf()
def visualize_type():
"""Visualize data by category in a bar graph"""
data_file = prs.parse(MY_FILE, ",")
counter = Counter(item["Category"] for item in data_file)
labels = tuple(counter.keys())
xlocations = na.array(range(len(day))) + 0.5
width = 0.5
plt.bar(xlocations, counter.values(), width=width)
plt.xticks(xlocations + width / 2, labels, rotation=90)
plt.subplots_adjust(bottom=0.4)
plt.rcParams['figure.figsize'] = 12, 8
plt.savefig("Type.png")
plt.clf()
def frequent_customer():
order_file = p.parse(BELLHOP_ORDER, ",")
customer_em = []
ems = set()
for item in order_file:
if (item['charges_verified'] == "Yes" and item['deposit_paid'] != ''):
customer_em.append(item['customer_email'])
for email in customer_em:
if (customer_em.count(email) > 2
and item['customer_email'] == email
and item['move_date'] > "2014-11-10"
and item['customer_review_score'] > 4.8):
ems.add(email)
e = list(ems)
for i in e:
print i
def truck_moves():
order_file = p.parse(BELLHOP_ORDER, ",")
begin_date = raw_input("Start date: ")
end_date = raw_input("End date: ")
counter = Counter(
item["truck_required"] for item in order_file
if item["booked_at_date"] >= begin_date
and item["booked_at_date"] <= end_date and item["cancelled"] != "Yes")
counter2 = Counter(item["charges_verified"] for item in order_file
if item["booked_at_date"] >= begin_date
and item["booked_at_date"] <= end_date)
truck_list = counter["Yes"]
verified_moves = counter2["Yes"]
print truck_list
print verified_moves
def calls_by_csr():
call_file = p.parse(CALL_DATA, ",")
begin_date = raw_input("Start date (Month dd, yyyy): ")
end_date = raw_input("End date( Month dd, yyyy): ")
ice_hours = raw_input("Ice Hours: ")
joey_hours = raw_input("Joey Hours: ")
branton_hours = raw_input("Branton Hours: ")
bj_hours = raw_input("BJ Hours: ")
chuck_hours = raw_input("Chuck Hours: ")
averyhart_hours = raw_input("Averyhart Hours: ")
jacob_hours = raw_input("Jacob Hours: ")
counter = Counter(
item["Agent"] for item in call_file
if item["Date/Time"] >= begin_date and item["Date/Time"] <= end_date
and item["Call Status"] == "Completed" and item["Minutes"] > "1")
call_list = [(float(counter["Brent Gauthier"]) / float(bj_hours)),
(float(counter["Joey Doughty"]) / float(joey_hours)),
(float(counter["Matt Intemann"]) / float(ice_hours)),
(float(counter["Branton Phillips"]) / float(branton_hours)),
(float(counter["Matt Averyhart"]) / float(averyhart_hours)),
(float(counter["Jacob Ellis"]) / float(jacob_hours)),
(float(counter["Charles Marczynski"]) / float(chuck_hours))]
csr_list = tuple(
["BJ", "Joey", "Iceman", "Branton", "Averyhart", "Jacob", "Chuck"])
xlocations = na.array(range(len(csr_list))) + 0.5
width = 0.5
rects1 = plt.bar(xlocations, call_list, width, color='green')
plt.xticks(xlocations + width / 2, csr_list, rotation=90)
plt.subplots_adjust(bottom=0.2)
plt.rcParams['figure.figsize'] = 12, 12
plt.suptitle("Inbound & Outboud Calls Per Hour Worked From " + begin_date +
" to " + end_date,
fontsize=12)
plt.ylabel("Inbound & Outboud Calls Per Hour Worked > 1 min", fontsize=12)
autolabel(rects1)
plt.savefig("phone_calls_per_hour_graph.png")
plt.clf()
def booked_moves_by_csr():
order_file = p.parse(BELLHOP_ORDER, ",")
print order_file
begin_date = raw_input("Start date: ")
end_date = raw_input("End date: ")
counter = Counter(
item["booked_by"] for item in order_file
if item["booked_at_date"] >= begin_date
and item["booked_at_date"] <= end_date and item["deposit_paid"] != "")
booked_list = [
counter["*****@*****.**"],
counter["*****@*****.**"], counter["*****@*****.**"],
counter["*****@*****.**"],
counter["*****@*****.**"],
counter["*****@*****.**"],
counter["*****@*****.**"],
counter["*****@*****.**"],
counter["*****@*****.**"],
counter["*****@*****.**"]
]
csr_list = tuple([
"Iceman", "Joey", "BJ", "Chuck", "Jacob", "Justin", "Tisha", "Olivia",
"Aaron", "Stacy"
])
xlocations = na.array(range(len(csr_list))) + 0.5
width = 0.5
rects1 = plt.bar(xlocations, booked_list, width, color='green')
plt.xticks(xlocations + width / 2, csr_list, rotation=90)
plt.subplots_adjust(bottom=0.2)
plt.rcParams['figure.figsize'] = 12, 12
plt.suptitle("Booked Moves from " + begin_date + " to " + end_date,
fontsize=12)
plt.ylabel("Moves Booked", fontsize=12)
autolabel(rects1)
plt.savefig("booked_moves_bar_graph.png")
plt.clf()
def total_inbound_calls():
inbounds = []
completed = []
abandoned_queue = []
left_voicemail = []
abandoned_unknown = []
abandoned_voicemail = []
call_file = p.parse(CALL_DATA, ",")
begin_date = "August 1, 2014"
end_date = "August 31, 2014"
for item in call_file:
if item['Wait Time'] != '':
inbounds.append(float(item['Wait Time']))
if item['Call Status'] == "Completed" and item[
'Agent'] == "Voicemail":
left_voicemail.append(float(item['Wait Time']))
elif item['Call Status'] == "Abandoned In Queue":
abandoned_queue.append(float(item['Wait Time']))
elif item['Call Status'] == "Completed" and item[
'Agent'] != "Voicemail":
completed.append(float(item['Wait Time']))
elif item['Call Status'] == "Abandoned Unknown" or item[
'Call Status'] == "Abandoned":
abandoned_unknown.append(float(item['Wait Time']))
elif item['Call Status'] == "Abandoned In Voicemail":
abandoned_voicemail.append(float(item['Wait Time']))
total_inbound = float(len(inbounds))
aw_in = float(na.average(inbounds))
voicemails = float(len(left_voicemail))
per_vm = (voicemails / total_inbound) * 100
aw_vm = float(na.average(left_voicemail))
hangup_voicemail = float(len(abandoned_voicemail))
per_hvm = (hangup_voicemail / total_inbound) * 100
aw_hvm = float(na.average(abandoned_voicemail))
complete_calls = float(len(completed))
per_cc = (complete_calls / total_inbound) * 100
aw_cc = float(na.average(completed))
hangup_queue = float(len(abandoned_queue))
per_hq = (hangup_queue / total_inbound) * 100
aw_hq = float(na.average(abandoned_queue))
aban_un = float(len(abandoned_unknown))
aw_un = float(na.average(abandoned_unknown))
per_un = (aban_un / total_inbound) * 100
print "---------" * 8
print "Inbound Call Data from %s to %s" % (begin_date, end_date)
print "---------" * 8
print "Total Inbound Calls: %.2f" % total_inbound
print "Avg Wait For All Inbound: %.2f seconds" % aw_in
print "---------" * 8
print "\tCalls Completed (%.2f%% of Total Inbound): %.2f" % (
per_cc, complete_calls)
print "\t\tAvg. Wait: %.2f seconds" % aw_cc
print "\tVoicemails Left (%.2f%% of Total Inbound): %.2f" % (per_vm,
voicemails)
print "\t\tAvg. Wait: %.2f seconds" % aw_vm
print "\tHung up in Voicemail (%.2f%% of Total Inbound): %.2f" % (
per_hvm, hangup_voicemail)
print "\t\tAvg. Wait: %.2f seconds" % aw_hvm
print "\tHung up in Queue (%.2f%% of Total Inbound): %.2f" % (per_hq,
hangup_queue)
print "\t\tAvg. Wait: %.2f seconds" % aw_hq
print "\tAbandoned Unknown (%.2f%% of Total Inbound): %.2f" % (per_un,
aban_un)
print "\t\tAvg. Wait: %.2f seconds" % aw_un
from collections import Counter
import csv
import numpy as np
import p1_parse as p
from bookedmoves import BELLHOP_ORDER
#first tip 2014-03-08
#2014-05-01 when refund data occured
order_file = p.parse(BELLHOP_ORDER, ",")
begin_date = '2014-09-01' #raw_input("Start date: ")
end_date = '2014-10-01' #raw_input("End date: ")
vm_counter = Counter(
item['charges_verified'] == "Yes" for item in order_file
if item['move_date'] >= begin_date and item['move_date'] <= end_date
and item['deposit_paid'] != '')
verified_moves = float(vm_counter[True])
print verified_moves
def bellhops_assigned():
hops = []
for item in order_file:
if (item['charges_verified'] == "Yes"
and item['move_date'] >= begin_date
and item['move_date'] <= end_date
and item['deposit_paid'] != ''
and item['bellhops_assigned'] != "0"):
hops.append(float(item['bellhops_assigned']))
avg_hops_per_job = np.average(hops)
print "Average Bellhops Per Move: %.2f" % avg_hops_per_job
from collections import Counter
from operator import truediv
import datetime
import csv
import re
import p1_parse as p
from bookedmoves import BELLHOP_ORDER
TICKET_FILE = "../data/report-feed-export-2014-12-01-1626-429105ca2f-v2.csv"
order_file = p.parse(BELLHOP_ORDER, ",")
ticket_file = p.parse(TICKET_FILE, ",")
begin_date = raw_input("Start date (yyyy-mm-dd): ")
end_date = raw_input("End date (yyyy-mm-dd): ")
def job_fills_per_tyson(agent):
jobs_filled = []
markets_filled = []
agent_hours = float(raw_input("%s's hours: " % agent))
for item in ticket_file:
solved_date = item['Solved at']
agent_solved = item['Assignee']
sub = item["Subject"]
split_sub = re.split("\W+", sub)
if range(len(split_sub)) > range(0):
if (split_sub[0].lower() == 'fill' and solved_date >= begin_date
and solved_date <= end_date and agent == agent_solved):
jobs_filled.append(float(item['Summation column']))
if (split_sub[2] == 'San' or split_sub[2] == 'College'
or split_sub[2] == 'Baton' or split_sub[2] == 'Boca'
from collections import Counter
import re
import matplotlib.pyplot as plt
import numpy as na
import p1_parse as p
BELLHOP_ORDER = "../data/chart-300192.csv"
TICKETS_FILE = "../data/report-feed-export-2015-02-02-1331-429105ba40-v2.csv"
ZOPIMS_FILE = "../data/report592867-5j0ccic3ESQvL6Vl.csv"
PHONE_FILE = "../data/agent_availability.csv"
order_file = p.parse(BELLHOP_ORDER, ",")
ticket_file = p.parse(TICKETS_FILE, ",")
zopim_file = p.parse(ZOPIMS_FILE, ",")
phone_call_file = p.parse(PHONE_FILE, ",")
begin_date = raw_input("Start date (yyyy-mm-dd): ")
end_date = raw_input("End date (yyyy-mm-dd): ")
agts = []
point = []
def point_function(agent):
agent_hours = float(raw_input("%s's hours: " % agent))
thank_you_vids = []
tix_sovled = []
jobs_filled = []
moves = []
touches = []
from collections import Counter import datetime import csv import p1_parse as p from bookedmoves import BELLHOP_ORDER order_database = p.parse(BELLHOP_ORDER, ",") def pay_structure(): for item in order_database: booked_datetime = (datetime.datetime.strptime(item['booked_at_date'] +' '+item['booked_at_time'], '%Y-%m-%d %I:%M:%S %p %Z')) move_datetime = (datetime.datetime.strptime(item['move_date'] +' '+item['move_time'], '%Y-%m-%d %I:%M:%S %p %Z')) captain_claim_datetime = (datetime.datetime.strptime(item['captain_claim_time'] +' '+item['captain_claim_time'], '%Y-%m-%d %I:%M:%S %p %Z') wingman_claim_datetime = (datetime.datetime.strptime(item['wingman_claim_time'] +' '+item['captain_claim_time'], '%Y-%m-%d %I:%M:%S %p %Z') if booked_datetime - move_datetime <= datetime.timedelta(hours=12): #Captain and Wingman for same day jobs captain_pay = 18.0 wingman_pay = 16.0 return (captain_pay, wingman_pay) if (booked_datetime - move_datetime <= datetime.timedelta(days=7) and booked_datetime - move_datetime > datetime.timedelta(hours=12)): pay_decrease_interval = (booked_datetime - move_datetime)/6
from collections import Counter
from operator import truediv
import datetime
import csv
import p1_parse as p
from bookedmoves import BELLHOP_ORDER
BELLHOP_PROFILES = "../data/2014_10_31_1752_bellhops_export.csv"
order_file = p.parse(BELLHOP_ORDER, ",")
bellhops_file = p.parse(BELLHOP_PROFILES, ',')
begin_date = "2014-05-01"
end_date = "2014-10-31"
def movers_in_cities(market_input):
unique_hops = set()
for item in order_file:
markets = item['market'] + ',' + ' ' + item['state']
if (item['charges_verified'] == "Yes"
and item['move_date'] >= "2014-02-01"
and item['move_date'] <= "2014-10-15"
and item['deposit_paid'] != '' and markets == market_input
and item['bellhops_assigned'] != ''
and item['bellhops_assigned'] > '0'):
unique_hops.add(item['captain'])
wing_list = item['wingmen'].split()
for i in wing_list:
unique_hops.add(i)
total = len(unique_hops)
return total
def market_performance(): order_file = p.parse(BELLHOP_ORDER, ",") market =
from collections import Counter import csv import matplotlib.pyplot as plt import numpy.numarray as na import p1_parse as p CHAMBLISS_FILE = "../data/DMP20-Table 1.csv" chambliss = p.parse(CHAMBLISS_FILE, ",") def vol_report(): bus = [] num_vol_hours_bus = [] num_vols_bus = [] chruch = [] foundation = [] individual = [] organization = [] school = [] for item in chambliss: if item['ContactType'] == "Business": if item['Company'] not in bus: bus.append(item['Company']) for item in bus: num_vols_bus.append(sum(int(item['Number of volunteers involved']))) print bus print num_vols_bus #bus_sort = sorted(bus)
def main():
data = p.parse(p.MY_FILE, ",")
return create_map(data)
