def startlog():
id = enteruser.id
x = time.localtime()
sec = x.tm_sec
min = x.tm_min
hour = x.tm_hour + 1
day = x.tm_mday
date = f"{x.tm_mon}-{x.tm_mday}-{x.tm_year}"
starttime = (day * 86400) + (hour * 3600) + (min * 60) + sec
updatestart = "UPDATE log SET start = ?, date = ? WHERE ID = ?"
c.execute(updatestart, (starttime, date, id,))
conn.commit()
I have this function startlog, and a clone of it endlog.
My database log is consisted of (name, starttime, endtime, date)
Is there any way to keep track of the changes?
Desired output:
Name / Time / Date
x / time1 / date1
x / time2 / date2
I tried creating a list so everytime I'm calling out the function it will append on the list but it disappears after the session.
I used csv for my case since it's just a personal project. I used columns like ID/Time in / Time out / Total Time and used ID to determine which value to display. This is the snippet of my code (using tkinter for gui)
def csvwrite():
with open ('test.cvs', 'a', newline="") as csvfile:
writer = csv.writer(csvfile)
tup1 = (enteruser.id, log.start, log.end)
writer.writerow(tup1)
csvfile.close()
def csvread():
with open('test.cvs', 'r') as csvfile:
reader = csv.reader(csvfile)
filtered = filter(filterer, reader)
res = []
for i in filtered:
print(i)
historylbl = Label(historyWindow.historywndw, text = i)
historylbl.pack()
Related
Good morning as per the attached code, I would like to create a function that if the work shift (Bezahltezeit variable is showed in the Überziet column in Excel) is more than 10 hours, a 25% surcharge is created for every minute worked after ten hours.
I should have a result in minutes so that I can then add it up and convert it to excel.
Also in the nachtzeit column the 10% surcharge is calculated for every minute worked between 10 am and 6 am, as you see in the table it is transcribed in hours and minutes, I should have the result in minutes again so that I can add it up and convert to excel.
The function was created with the help of user #constantstranger whom I thank again!
Thanks
Year = int(IcsStartData.year)
Month = int(IcsStartData.month)
Day = int(IcsStartData.day)
StartH = int(IcsStartData.hour)
StartM = int(IcsStartData.minute)
EndH = int(IcsEndData.hour)
EndM = int(IcsEndData.minute)
currentDateTime = datetime.now()
Date_Time_Stamp = currentDateTime.strftime("%d%m%Y%H%M")
Current_DateTime = currentDateTime.strftime("%d.%m.%Y %H:%M")
Datetime = IcsStartData.strftime("%d.%m.%Y")
StartTime = IcsStartData.strftime("%H.%M")
EndTime = IcsEndData.strftime("%H.%M")
Schichtdauer = IcsSchichtDauer.strftime("%H.%M")
Bezahltezeit = IcsBezahlteZeit.strftime("%H.%M")
Bezahltezeit_Stunden = float(IcsBezahlteZeit.strftime("%H"))
Bezahltezeit_Minuten = float(IcsBezahlteZeit.strftime("%M"))
Terminaldata = IcsEndData.strftime("%d.%m.%Y")
EndDay = int(IcsEndData.day)
EndMonth = int(IcsEndData.month)
EndYear = int(IcsEndData.year)
def excelworking():
endTime = IcsEndData
startTime = IcsStartData
def getRegularAndBonusHours(startTime, endTime):
if endTime < startTime:
raise ValueError(f'endTime {endTime} is before startTime {startTime}')
startDateStr = startTime.strftime("%d.%m.%Y")
bonusStartTime = datetime.strptime(startDateStr + " " + "20:00", "%d.%m.%Y %H:%M")
prevBonusEndTime = datetime.strptime(startTime.strftime("%d.%m.%Y") + " " + "06:00", "%d.%m.%Y %H:%M")
bonusEndTime = prevBonusEndTime + timedelta(days=1)
NachtArbeitZeit = timedelta(days=0)
dienstdauer = endTime - startTime
hours = dienstdauer.total_seconds() // 3600
if hours > 24:
fullDays = hours // 24
NachtArbeitZeit += fullDays * (bonusEndTime - bonusStartTime)
endTime -= timedelta(days=fullDays)
if startTime < prevBonusEndTime:
NachtArbeitZeit += prevBonusEndTime - startTime
if endTime < prevBonusEndTime:
NachtArbeitZeit -= prevBonusEndTime - endTime
if startTime > bonusStartTime:
NachtArbeitZeit -= startTime - bonusStartTime
if endTime > bonusStartTime:
NachtArbeitZeit += min(endTime, bonusEndTime) - bonusStartTime
return dienstdauer, NachtArbeitZeit
def getHours(startTime, endTime, extraFraction):
dienstdauer, NachtArbeitZeit = getRegularAndBonusHours(startTime, endTime)
delta = dienstdauer + NachtArbeitZeit * extraFraction
return delta
def testing(start, end):
dienstdauer, NachtArbeitZeit = getRegularAndBonusHours(start, end)
def getHoursRoundedUp(delta):
return delta.days * 24 + delta.seconds // 3600 + (1 if delta.seconds % 3600 else 0)
regularHours, nachtszulage = getHoursRoundedUp(dienstdauer), getHoursRoundedUp(NachtArbeitZeit)
# print(f'start {start}, end {end}, nachtszulage {nachtszulage}, Nachstüberzeit {NachtArbeitZeit / 60 * 10} dienstdauer: {dienstdauer} {NachtArbeitZeit}')
# Writing on a EXCEL FILE
filename = (f"{myPath}/Monatsplan {username} {month} {year}.xlsx")
emptycell = ' '
wegzeiten = (int('13'))
try:
wb = load_workbook(filename)
ws = wb.worksheets[0] # select first worksheet
except FileNotFoundError:
headers_row = ['Datum','Dienst','Funktion','Von','Bis','Schichtdauer','Bezahlte Zeit (Studen)','Bezahlte Zeit (Minuten)','Zeit Konvertierung','Überzeit (ab 10 St.)','Nachtzeitzuschlag.','Nachtdienstentschädigung','Wegzeiten']
wb = Workbook()
ws = wb.active
ws.append(headers_row)
wb.save(filename)
ws.append([f'{Datetime}',f'{string1}'f'{tagesinfo2}',f'{soup_funktion}',f'{StartTime}',f'{EndTime}',f'{Schichtdauer}',f'{Bezahltezeit_Stunden}',f'{Bezahltezeit_Minuten}',f'{emptycell}',f'{emptycell}',f'{NachtArbeitZeit / 60 * 10}',f'{nachtszulage}',f'{wegzeiten}'])
for cols in ws.iter_cols( ):
if cols[-1].value:
cols[-1].border = Border(left=Side(style='thin'),right=Side(style='thin'),top=Side(style='thin'),bottom=Side(style='thin'))
cols[-1].number_format = '0.00'
wb.save(filename)
wb.close()
I'm trying to get the data from google search console in behalf of the user once they login it returns the access_token and refresh_token by using the access_token or refresh_token how to get the Google Search Console data (imperssion,click,pages).
Same way i am getting the data from Google Analytics but in google search console it's not possible.
def extract_data(site, creds, num_days, output):
domain_name = get_domain_name(site)
create_project(domain_name)
full_path = domain_name + '/' + output
current_dates = get_dates_from_csv(full_path)
webmasters_service = authorize_creds(creds)
# Set up Dates
end_date = datetime.date.today() - relativedelta.relativedelta(days=3)
start_date = end_date - relativedelta.relativedelta(days=num_days)
delta = datetime.timedelta(days=1) # This will let us loop one day at the time
scDict = defaultdict(list)
while start_date <= end_date:
if current_dates is not None and current_dates.str.contains(
datetime.datetime.strftime(start_date, '%Y-%m-%d')).any():
start_date += delta
else:
# print('Start date at beginning: %s' % start_date)
maxRows = 25000 # Maximum 25K per call
numRows = 0 # Start at Row Zero
status = '' # Initialize status of extraction
# print("status status status status",status)
while (status != 'Finished'): # Test with i < 10 just to see how long the task will take to process.
request = {
'startDate': datetime.datetime.strftime(start_date, '%Y-%m-%d'),
'endDate': datetime.datetime.strftime(start_date, '%Y-%m-%d'),
'dimensions': ['date', 'page', 'query'],
'rowLimit': maxRows,
'startRow': numRows
}
response = execute_request(webmasters_service, site, request)
try:
# Process the response
for row in response['rows']:
scDict['date'].append(row['keys'][0] or 0)
scDict['page'].append(row['keys'][1] or 0)
scDict['query'].append(row['keys'][2] or 0)
scDict['clicks'].append(row['clicks'] or 0)
scDict['ctr'].append(row['ctr'] or 0)
scDict['impressions'].append(row['impressions'] or 0)
scDict['position'].append(row['position'] or 0)
# print('successful at %i' % numRows)
except:
print('error occurred at %i' % numRows)
# Add response to dataframe
df = pd.DataFrame(data=scDict)
df['clicks'] = df['clicks'].astype('int')
df['ctr'] = df['ctr'] * 100
df['impressions'] = df['impressions'].astype('int')
df['position'] = df['position'].round(2)
print('Numrows at the start of loop: %i' % numRows)
try:
numRows = numRows + len(response['rows'])
except:
status = 'Finished'
print('Numrows at the end of loop: %i' % numRows)
if numRows % maxRows != 0:
status = 'Finished'
start_date += delta
print('Start date at end: %s' % start_date)
write_to_csv(df, full_path)
return df
This is code i am getting in google search console this code using the webmasters_service = authorize_creds(creds) method but i want to access using access_token or refresh token.
This is the code used in google analytics.
def google_analytics_reporting_api_data_extraction(viewID, dim, met, start_date,
end_date, refresh_token,
transaction_type, goal_number,
condition):
viewID = viewID;
dim = dim;
met = met;
start_date = start_date;
end_date = end_date;
refresh_token = refresh_token;
transaction_type = transaction_type;
condition = condition
goal_number = goal_number
viewID = "".join(['ga%3A', viewID])
if transaction_type == "Goal":
met1 = "%2C".join([re.sub(":", "%3A", i) for i in met]).replace("XX", str(goal_number))
elif transaction_type == "Transaction":
met1 = "%2C".join([re.sub(":", "%3A", i) for i in met])
dim1 = "%2C".join([re.sub(":", "%3A", i) for i in dim])
credentials = client.OAuth2Credentials(
access_token=None, client_id=client_id, client_secret=client_secret, refresh_token=refresh_token,
token_expiry=3600, token_uri=GOOGLE_TOKEN_URI, user_agent='my-user-agent/1.0', revoke_uri=GOOGLE_REVOKE_URI)
credentials.refresh(httplib2.Http())
rt = (json.loads(credentials.to_json()))['access_token']
api_url = "https://www.googleapis.com/analytics/v3/data/ga?ids="
url = "".join(
[api_url, viewID, '&start-date=', start_date, '&end-date=', end_date, '&metrics=', met1, '&dimensions=',
dim1, '&max-results=1000000', condition, '&access_token=', rt])
data = pd.DataFrame()
dataa = pd.DataFrame()
users = []
final_date = []
# try:
r = requests.get(url)
# print("r values",list((r.json())['rows']))
# print("start_date",start_date)
start = datetime.datetime.strptime(start_date, "%Y-%m-%d")
end = datetime.datetime.strptime(end_date, "%Y-%m-%d")
date_generated = [start + datetime.timedelta(days=x) for x in range(0, (end - start).days)]
for each in date_generated:
date_value = each.date()
url = "".join(
[api_url, viewID, '&start-date=', str(each.date()), '&end-date=', str(each.date()), '&metrics=', met1,
'&dimensions=',
dim1, '&max-results=1000000', condition, '&access_token=', rt])
rr = requests.get(url)
dataa = pd.DataFrame(list((rr.json())['rows']))
users.append(dataa[0][0])
final_date.append(str(date_value))
# print("data and users", users, final_date)
data = pd.DataFrame(list((r.json())['rows']))
try:
data = pd.DataFrame(list((r.json())['rows']), columns=[re.sub("ga:", "", i) for i in met])
# data['date'] = start_date
# dim_data = pd.DataFrame(list((r.json())['rows']), columns=[re.sub("ga:", "", i) for i in dim])
return data, users, final_date
except:
print((r.json()))
In the above code by using refresh_token we access the data from google analytics. Like this way only i want the code in google search console.
Please help me out
I'm using Python 3.9 and Django 3.2. I have this price model
class Price(models.Model):
id = models.UUIDField(primary_key=True, default=uuid.uuid4, editable=False)
price = models.FloatField(null=False)
created = models.DateTimeField(null=False, default=datetime.now)
If I want to get the price per hour over the last 24 hours, I can run a method like this
def _get_prices_per_time_slice(self, last_hours=24):
now = timezone.now()
end_time = now.replace(second = 0, microsecond = 0)
start_time = end_time - timedelta(hours=last_hours)
qset = Price.objects.filter(
created__range=[start_time, end_time],
created__minute=end_time.minute
).values('price')
return [r['price'] for r in qset]
but let's say I want to get the price every last X hours.
def _get_prices_per_time_slice(self, last_hours=24, time_slice_in_hours=4):
so if the current time is midnight (and zero seconds and minutes), I would want to get the prices for midnight, 8 pm, 4 pm, noon, 8 am and 4 am. How do I add a filter to screen for prices every X hours?
The range function in python helps to specify the incremental step value
SYNTAX : range(start, stop, step)
x = range(3, 20, 4)
for n in x:
print(n)
#Gives output
>>> 3
>>> 7
>>> 11
>>> 15
>>> 19
Just rewrite created_range to add the step value time_slice_in_hours
created__range=[start_time, end_time, time_slice_in_hours]
OPTION 1
def _get_prices_per_time_slice(self, last_hours= 24,time_slice_in_hours=4):
now = timezone.now()
end_time = now.replace(second = 0, microsecond = 0)
start_time = end_time - timedelta(hours=last_hours)
qset = Price.objects.filter(
created__range=[start_time, end_time, time_slice_in_hours],
created__minute=end_time.minute
).values('price')
return [r['price'] for r in qset]
However the syntax in django query set api official documentation fails to mention step parameter in __range() function i.e., it might not be supported by created__range.
OPTION 2
In that case you can use the below function where you can calculate time range x_time_slice_list using Python’s DateTimeRange function(official documentation) and evaluate to created__in
from datetimerange import DateTimeRange
from dateutil.relativedelta import relativedelta
def _get_prices_per_time_slice(self, last_hours= 24,time_slice_in_hours=4):
now = timezone.now()
end_time = now.replace(second = 0, microsecond = 0)
start_time = end_time - timedelta(hours=last_hours)
x_time_slice_list= []
time_range = DateTimeRange(start_time, end_time)
for value in time_range.range(relativedelta(hours=+time_slice_in_hours)):
x_time_slice_list.append(value)
qset = Price.objects.filter(
created__in= x_time_slice_list,
created__minute=end_time.minute
).values('price')
return [r['price'] for r in qset]
You could achieve this by handling the time slice filtering in python.
What I mean is if you take the function you have. i.e.
def _get_prices_per_time_slice(self, last_hours=24):
now = timezone.now()
end_time = now.replace(second = 0, microsecond = 0)
start_time = end_time - timedelta(hours=last_hours)
qset = Price.objects.filter(
created__range=[start_time, end_time],
created__minute=end_time.minute
).values('price')
return [r['price'] for r in qset]
And rewrite it as follows you to return the created data and handle the filtering in the return list comprehension.
def _get_prices_per_time_slice(self, last_hours=24, time_slice_in_hours=4):
now = timezone.now()
end_time = now.replace(second = 0, microsecond = 0)
start_time = end_time - timedelta(hours=last_hours)
qset = Price.objects.filter(
created__range=[start_time, end_time],
created__minute=end_time.minute
).values('price', 'created')
return [r.get('price') for r in qset if r.get('created').hour % time_slice_in_hours == 0]
I ran the PyCharm profiler on my code and go the following call graph.
new_method (second from the left) gets called however I do not have any function called new_method in my code. I assume it comes from where I assign close_price in the code below. If so, is there a better way of sectioning data from a dataframe that does not take so long?
def base_algo(conn):
#start_date = datetime.strptime("2000-09-22", "%Y-%m-%d")
start_date = datetime.strptime("2020-07-02", "%Y-%m-%d")
end_date = datetime.strptime("2020-07-23", "%Y-%m-%d")
current_date = start_date
while current_date < end_date:
historical_data = select_historical_data(conn, current_date)
buy_sell_signal = pd.DataFrame()
for stock in historical_data["stock"].unique():
close_price = historical_data[historical_data["stock"] == stock].close_val
slope_50 = calculate_slope(close_price, 50)
slope_200 = calculate_slope(close_price, 200)
to_append = {"stock":stock, "slope_50":slope_50, "slope_200":slope_200}
buy_sell_signal = buy_sell_signal.append(to_append, ignore_index=True)
current_date = current_date + timedelta(days=1)
I have the below script.
I am a bit stuck with this specific piece:
datex = datetime.datetime.strptime(df1.start_time,'%Y-%m-%d %H:%M:%S')
I can't figure out how to extract the actual value from the start_time field & store it in the datex variable.
Can anyone help me please?
while iters <10:
time_to_add = iters * 900
time_to_checkx = time_to_check + datetime.timedelta(seconds=time_to_add)
iters = iters + 1
session = 0
for row in df1.rdd.collect():
datex = datetime.datetime.strptime(df1.start_time,'%Y-%m-%d %H:%M:%S')
print(datex)
filterx = df1.filter(datex < time_to_checkx)
session = session + filterx.count()
print('current session value' + str(session))
print(session)
Check this out. I have converted your for loop in general. If you can get me more info on iters variable or the explanation of how you want it to work:
import pyspark.sql.functions a F
spark_date_format = "YYYY-MM-dd hh:mm:ss"
session = 0
time_to_checkx = time_to_check + datetime.timedelta(seconds=time_to_add)
df1 = df1.withColumn('start_time', F.to_timestamp(F.col(date_column), spark_date_format))
filterx = df1.filter(df1.start_time < time_to_checkx)
session = session + filterx.count()