def timeblockrange(start,end,exclude=None) :
start = start
end = end
if exclude is None :
exclude_list = [7356014]
else :
exclude_list = exclude
end_plus1 = datetime.strftime(datetime.strptime(end,'%Y-%m-%d') + timedelta(1), '%Y-%m-%d')
headers = wheniworktoken()
params = (
('start', start),
('end', end_plus1),
)
r = requests.get('https://api.wheniwork.com/2/times', headers=headers, params=params)
j = r.json()
j = j['times']
data = {k:[] for k,v in j[0].items()}
for i in j :
for k,v in i.items():
try :
data[k].append(v)
except : continue
dfraw = pd.DataFrame(data)
dfdata = dfraw.filter(['user_id','position_id','start_time','end_time','length','hourly_rate','notes'])
dfdata = dfdata[~(dfdata['position_id'].isin(exclude_list))].drop(columns=['position_id'])
dfdata['start_time'] = dfdata['start_time'].str.extract('(..\s...\s....\s..:..:..)')
dfdata['start_time'] = pd.to_datetime(dfdata['start_time'],infer_datetime_format=True)
dfdata['end_time'] = dfdata['end_time'].str.extract('(..\s...\s....\s..:..:..)')
dfdata['end_time'] = dfdata['end_time'].replace(pd.NaT,'{} 20:00:00'.format(start)) # in case someone forgets to clock out at 8pm
dfdata['end_time'] = pd.to_datetime(dfdata['end_time'],infer_datetime_format=True)
dfdata['Date'] = dfdata['end_time'].astype('str').str.extract('(....-..-..)')
hour_list = []
for index,row in dfdata.iterrows() :
time = pd.date_range(start=row['start_time'], end=row['end_time'],freq='S')
s = pd.Series(0.00027778,index=time,name='hours')
s = s.resample('30T',label='right',closed='right').sum()
hour_list.append(s)
dftemp2 = pd.concat(hour_list,axis=1)
dftemp2['number_agents'] = (dftemp2.sum(axis=1))*2 # Number of hours = number of agents
df = dftemp2['number_agents']
df_call = callpatternrange(start='2018-05-01', end='2018-09-15').drop(columns=['Date','rank'])
df_final = df_call.join(df,how='inner')
df_final = df_final.drop(columns=['SL Abandoned','Abandoned Calls'])
df_final = df_final.apply(pd.to_numeric, errors='ignore')
return df_final
def workedshiftblock(start=None,end=None,exclude=None) :
if start is None :
start = datetime.strftime(datetime.now() - timedelta(1), '%Y-%m-%d')
else :
start = start
if end is None :
end_plus1 = datetime.strftime(datetime.strptime(start,'%Y-%m-%d') + timedelta(1), '%Y-%m-%d')
else :
end_plus1 = datetime.strftime(datetime.strptime(end,'%Y-%m-%d') + timedelta(1), '%Y-%m-%d')
if exclude is None :
exclude_list = [7356014]
else :
exclude_list = exclude
headers = wheniworktoken()
params = (
('start', start),
('end', end_plus1),
)
r = requests.get('https://api.wheniwork.com/2/times', headers=headers, params=params)
j = r.json()
j = j['times']
data = {k:[] for k,v in j[0].items()}
for i in j :
for k,v in i.items():
try :
data[k].append(v)
except : continue
dfraw = pd.DataFrame(data)
dfdata = dfraw.filter(['user_id','position_id','start_time','end_time','length','hourly_rate','notes'])
dfdata = dfdata[~(dfdata['position_id'].isin(exclude_list))].drop(columns=['position_id'])
dfdata['start_time'] = dfdata['start_time'].str.extract('(..\s...\s....\s..:..:..)')
dfdata['start_time'] = pd.to_datetime(dfdata['start_time'],infer_datetime_format=True)
dfdata['end_time'] = dfdata['end_time'].str.extract('(..\s...\s....\s..:..:..)')
dfdata['end_time'] = dfdata['end_time'].replace(pd.NaT,'{} 20:00:00'.format(start)) # in case someone forgets to clock out at 8pm
dfdata['end_time'] = pd.to_datetime(dfdata['end_time'],infer_datetime_format=True)
return start, dfdata
print(str(['Hey'][0]))
list = np.random.binomial(20, 0.5, 10000)
print(len(list))
print(sum((i / i) for i in list if i >= 15) / len(list))
t = np.random.binomial(1000, 0.01)
print(t)
v = np.random.binomial(1, 0.01, 1000)
print(sum(v))
print(''.join(['h', 'e', 'y']))
import requests
headers = wheniworktoken()
r = requests.get('https://api.wheniwork.com/2/positions', headers=headers)
j = r.json()
j = j["positions"]
data = {k: [] for k, v in j[0].items()}
for i in j:
for k, v in i.items():
try:
data[k].append(v)
except:
continue
df = pd.DataFrame(data)
