async def insert_test_marker(self, ctx, n_from: int, n_to: int):
fom = datetime.datetime.now() - timedelta.Timedelta(days=n_from)
to = datetime.datetime.now() - timedelta.Timedelta(days=n_to)
await utils.db.insertOne(
f"analytics.text.a{ctx.guild.id}.cache_markers", {
"to": to,
"from": fom
})
def create_event(request):
family_session = request.session['family_session']
fam = Family.objects.get(nameofFamily=family_session)
credentialsDanu = pickle.load(open("token.pkl", "rb"))
serviceDanu = build("calendar", "v3", credentials=credentialsDanu)
scopes = ['https://www.googleapis.com/auth/calendar']
start_time_str = request.POST['start_time']
summary = request.POST['summary']
duration = int(request.POST['duration'])
description = request.POST['description']
location = request.POST['location']
matches = list(datefinder.find_dates(start_time_str))
if len(matches):
start_time = matches[0]
end_time = start_time + timedelta.Timedelta(hours=duration)
event = {
'summary': summary,
'location': location,
'description': description,
'start': {
'dateTime': start_time.strftime("%Y-%m-%dT%H:%M:%S"),
'timeZone': "GMT+00:00",
},
'end': {
'dateTime': end_time.strftime("%Y-%m-%dT%H:%M:%S"),
# 'dateTime': end_time,
'timeZone': "GMT+00:00"
},
'reminders': {
'useDefault':
False,
'overrides': [
{
'method': 'email',
'minutes': 24 * 60
},
{
'method': 'popup',
'minutes': 10
},
],
},
}
created = serviceDanu.events().insert(calendarId=fam.calId,
body=event).execute()
print('CREATED :', created)
event = EventEntry(
summary=summary,
description=description,
location=location,
start_time=start_time_str,
duration=duration,
)
event.save()
fam.calEvents.add(event)
return redirect('calendar')
def get_token():
_entity = tokenEntity()
if _entity.validate_request(request):
_expires = timedelta.Timedelta(weeks=1)
_token = create_access_token(_entity.user, expires_delta=_expires)
_ret = {'access_token': _token}
return jsonify(_ret), 200
return jsonify({"message": "usuario o password errado"}), 401
async def pull_text_data_from_api(self, ctx, n=5):
today = datetime.datetime.now()
last_day = today - timedelta.Timedelta(days=n)
this_marker = {
"from": today,
"to": last_day,
"id": ctx.message.id,
}
query_periods = [this_marker]
query = {"from": {"$gte": last_day}}
cache_markers = await utils.db.find(
f"analytics.text.a{ctx.guild.id}.cache_markers", query)
for marker in cache_markers:
query_periods = insert_marker(marker, query_periods)
def to_string(m):
now = datetime.datetime.now()
return f"From {(now - m['from']).days} days go, To : {(now - m['to']).days} days go"
total_query_days = 0
for period in query_periods:
total_query_days += (period["from"] - period["to"]).days
newline = '\n'
await ctx.send(
f"Periods to query: {newline}{newline.join([to_string(p) for p in query_periods ])}"
+ '\n' + f"total: {total_query_days} days")
count = 0
for period in query_periods:
for channel in ctx.guild.text_channels:
try:
async for message in channel.history(
limit=None, after=period["to"],
before=period["from"]):
await self._log_message(message)
count += 1
except Exception as e:
print(e)
for marker in cache_markers:
await utils.db.deleteOne(
f"analytics.text.a{ctx.guild.id}.cache_markers", marker)
await utils.db.insertOne(
f"analytics.text.a{ctx.guild.id}.cache_markers", this_marker)
await ctx.send(f"Cached {count} messages :thumbsup:")
def create_event(start_time_str,
service,
summary,
duration=1,
attendees=None,
description=None,
location=None):
matches = list(datefinder.find_dates(start_time_str))
if len(matches):
start_time = matches[0]
end_time = start_time + timedelta.Timedelta(hours=duration)
event = {
'summary': summary,
'location': location,
'description': description,
'start': {
'dateTime': start_time.strftime("%Y-%m-%dT%H:%M:%S"),
'timeZone': "GMT+00:00",
},
'end': {
'dateTime': end_time.strftime("%Y-%m-%dT%H:%M:%S"),
'timeZone': "GMT+00:00"
},
'attendees': [
{
'email': attendees
},
],
'reminders': {
'useDefault':
False,
'overrides': [
{
'method': 'email',
'minutes': 24 * 60
},
{
'method': 'popup',
'minutes': 10
},
],
},
}
print('''*** %r event added:
With: %s
Start: %s
End: %s''' % (summary.encode('utf-8'), attendees, start_time, end_time))
return service.events().insert(calendarId='primary',
body=event,
sendNotifications=True).execute()
def get_game():
"""
do- parse an internet site to see which games of the nba were good
:return: return string that contains the wanted massage
"""
try:
fmt = '%Y-%m-%d %H:%M:%S %Z%z'
page = requests.get('http://stats.inpredictable.com/nba/preCap.php')
tree = html.fromstring(page.content)
# get games played in the day
games = tree.xpath('//a[@target="_blank"]/text()')
Excitement = tree.xpath(
'/html/body/div[5]/div/div/table/tbody/tr/td[4]/text()')
date = tree.xpath('/html/body/div[5]/div/span/text()')
web_date = date[0].split(',')[0].split()[5]
before = datetime.date.today() - timedelta.Timedelta(
minutes=1440) #24 hours
today_date = str(before.strftime(fmt)).split('-')[2][0:2]
#print (today_date)
#print (web_date)
#check if the games presented are relevant for today
if today_date != web_date:
return "no games today"
#create list of good games
c = 0
good_games = []
for i in Excitement:
if float(i) >= 9:
good_games.append(games[c + 1])
c += 1
#print (good_games)
#crete string
string = ""
if not good_games:
return "all games were bad"
for g in good_games:
string += g + " "
#print string
return string
except:
return "there were no games"
async def loop(self):
await self.bot.wait_until_ready()
while not self.bot.is_closed() and not self.dead:
hb = await utils.db.findOne("heartbeat", {})
now = datetime.datetime.now()
try:
last_hb = hb["datetime"]
except TypeError:
await log_heartbeat(now)
return
if last_hb < (now -
timedelta.Timedelta(minutes=3)): # hay downtime
await self.log_downtime(last_hb, now)
else: # todo piola
await log_heartbeat(now)
await asyncio.sleep(60 * 2)
print("hb loop down")
import pandas as pd
import datetime as dt
import timedelta as td
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
# 지점 코드
# 서울 인천 대구 대전 부산 울산 광주 제주 순서
locationList = [108, 112, 143, 133, 159, 152, 156, 184]
heightList = [0.7969, 0.6153, 1.3230, 0.7922, 1.1715, 2.1346, 1.2644, 0.5202]
date = (dt.datetime.today() - td.Timedelta(days=2)).strftime('%Y%m%d')
def data_snow(start, end, index):
url = "http://apis.data.go.kr/1360000/AsosDalyInfoService/getWthrDataList" \
"?serviceKey=LAEin4h5h2HeNf9fuSWuorK2uW5MyuvoiWeJL3uSRZivdAzWhtcrCECKzSKrU9Dfwe8W6tdNR24tDTBZEPYiEQ%3D%3D" \
"&numOfRows=999" \
"&dataCd=ASOS" \
"&dateCd=DAY" \
"&startDt=" + str(start) + \
"&endDt=" + str(end) + \
"&stnIds=" + str(locationList[index])
request = ul.Request(url)
response = ul.urlopen(request)
rescode = response.getcode()
weather = [[]]
async def info(self, ctx, *args):
n, member = await self.parse_params(ctx, args)
now = datetime.datetime.now()
query = {
"datetime": {
"$lt": now,
"$gte": now - timedelta.Timedelta(days=n)
},
"member_id": member.id,
"guild_id": member.guild.id
}
text_data = await utils.db.find(f"analytics.text.a{member.guild.id}",
query)
voice_data = await utils.db.find(f"analytics.voice.a{member.guild.id}",
query)
days = [now.date()]
for i in range(1, n + 1):
days.append((now - timedelta.Timedelta(days=i)).date())
days = list(reversed(days))
columns = ['#Messages', '#Characters', "#qj", "#qn", "#VcMins"]
df = pd.DataFrame(index=days)
for c in columns:
df[c] = 0
for datapoint in voice_data:
day = datapoint['datetime'].date()
df["#VcMins"][day] += datapoint["length_mins"]
for datapoint in text_data:
day = datapoint['datetime'].date()
df['#Messages'][day] += 1
df['#Characters'][day] += datapoint['length']
if datapoint['qn']:
df['#qn'][day] += 1
elif datapoint['qj']:
df['#qj'][day] += 1
bar_plot(df[["#Characters", "#VcMins"]], "g1.png", palette="hls")
bar_plot(df[["#qn", "#qj"]], "g2.png", palette="Set2")
with open('g1.png', 'rb') as f:
with open('g2.png', 'rb') as f2:
fs = [
discord.File(f, filename='Activity Graph.png'),
discord.File(f2, filename='QueueBot Usage Graph.png')
]
await ctx.send("User data for " + member.display_name + ":",
files=fs)
def getPermanentUrl(self, bucketname, filename):
try:
return self.__minioclient.presigned_get_object(bucketname,filename, expires=timedelta.Timedelta(days=7))
except ResponseError as err:
print(err)
def typhoonPredict(dateStart, dateEnd):
url = "http://apis.data.go.kr/1360000/AsosDalyInfoService/getWthrDataList" \
"?serviceKey=LAEin4h5h2HeNf9fuSWuorK2uW5MyuvoiWeJL3uSRZivdAzWhtcrCECKzSKrU9Dfwe8W6tdNR24tDTBZEPYiEQ%3D%3D" \
"&numOfRows=999" \
"&dataCd=ASOS" \
"&dateCd=DAY" \
"&startDt="+dateStart+ \
"&endDt="+dateEnd+ \
"&stnIds=184"
request = ul.Request(url)
response = ul.urlopen(request)
rescode = response.getcode()
if (rescode == 200):
responseData = response.read()
rD = xmltodict.parse(responseData)
rDJ = json.dumps(rD)
rDD = json.loads(rDJ)
size = int(rDD['response']['body']['totalCount'])
if (size != 1):
weather = [[0] * 8] * (size)
else:
weather = [[0] * 6]
date = dt.datetime.strptime(dateStart, '%Y%m%d').date()
if (size != 1):
weather[0] = ['date', '1', '2', '3', '4', '5', '6', '7']
else:
size = 2
for index in range(1, size):
try:
if (size == 2):
avgRhm = rDD['response']['body']['items']['item']['avgRhm']
else:
avgRhm = rDD['response']['body']['items']['item'][index][
'avgRhm']
# 평균 상대 습도
if avgRhm is None:
avgRhm = 0
except (TypeError, ValueError):
avgRhm = 0
try:
if (size == 2):
avgPs = rDD['response']['body']['items']['item']['avgPs']
else:
avgPs = rDD['response']['body']['items']['item'][index][
'avgPs']
# 평균 해면 기압
if avgPs is None:
avgPs = 0
except (TypeError, ValueError):
avgPs = 0
try:
if (size == 2):
avgPa = rDD['response']['body']['items']['item']['avgPa']
else:
avgPa = rDD['response']['body']['items']['item'][index][
'avgPa']
# 평균 현지 기압
if avgPa is None:
avgPa = 0
except (TypeError, ValueError):
avgPa = 0
try:
if (size == 2):
avgWs = rDD['response']['body']['items']['item']['avgWs']
else:
avgWs = rDD['response']['body']['items']['item'][index][
'avgWs']
# 평균 풍속
if avgWs is None:
avgWs = 0
except (TypeError, ValueError):
avgWs = 0
try:
if (size == 2):
sumRn = rDD['response']['body']['items']['item']['sumRn']
else:
sumRn = rDD['response']['body']['items']['item'][index][
'sumRn']
# 강수량
if sumRn is None:
sumRn = 0
except (TypeError, ValueError):
sumRn = 0
try:
if (size == 2):
avgTa = rDD['response']['body']['items']['item']['avgTa']
else:
avgTa = rDD['response']['body']['items']['item'][index][
'avgTa']
# 평균 기온
if avgTa is None:
avgTa = 0
except (TypeError, ValueError):
avgTa = 0
if (size != 2):
weather[index] = [
str(date), avgRhm, avgPs, avgPa, avgWs, sumRn, avgTa, 0
]
else:
weather[0] = [avgRhm, avgPs, avgPa, avgWs, sumRn, avgTa]
date = date + td.Timedelta(days=1)
return weather
dateGap = (endDate - startDate).days
date = startDate
for temp in range(0, dateGap + 1):
count = 1
while True:
if (str(date) == weather[count][0]):
weather[count][7] = 1
break
else:
count += 1
if (count > size):
count = 1
print('! restart ')
date = date + td.Timedelta(days=1)
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
weather = np.array(weather)
x = weather[1:, 1:7]
y = weather[1:, 7]
x = x.astype(np.float64)
y = y.astype(np.float64)
weather = np.nan_to_num(weather)
scaler = StandardScaler()
import timedelta
import GA
def reportDay(day):
# get analytics data for the day
# get adwords data for the day
# calculate roi for (in Google Ads class)
# Dynamic Remarketing
# SEM (Brand)
# SEM (Non-Brand)
GA.main()
LASTXDAYS = 1
today = datetime.datetime.now().date()
dayz = []
i = 0
while i < LASTXDAYS:
dayscount = i + 1
thatDay = today - timedelta.Timedelta(days=dayscount)
dayz.append(thatDay)
i = dayscount
print(dayz)
for d in dayz:
reportDay(d)
#!/usr/bin/env python
from datetime import datetime
import timedelta
dt1 = datetime.now()
dt2 = datetime.now() + timedelta.Timedelta(days=2, hours=2)
td = timedelta.Timedelta(days=2, hours=2)
print("seconds = %s" % td.total.seconds)
print("minutes = %s" % td.total.minutes)
print("hours = %s" % td.total.hours)
print("days = %s" % td.total.days)
def data_rain(start, end, index):
url = "http://apis.data.go.kr/1360000/AsosDalyInfoService/getWthrDataList" \
"?serviceKey=LAEin4h5h2HeNf9fuSWuorK2uW5MyuvoiWeJL3uSRZivdAzWhtcrCECKzSKrU9Dfwe8W6tdNR24tDTBZEPYiEQ%3D%3D" \
"&numOfRows=999" \
"&dataCd=ASOS" \
"&dateCd=DAY" \
"&startDt=" + str(start) + \
"&endDt=" + str(end) + \
"&stnIds=" + str(locationList[index])
request = ul.Request(url)
response = ul.urlopen(request)
rescode = response.getcode()
weather = [[]]
if (rescode == 200):
responseData = response.read()
rD = xmltodict.parse(responseData)
rDJ = json.dumps(rD)
rDD = json.loads(rDJ)
size = int(rDD['response']['body']['totalCount'])
weather = [[0] * 18] * (size)
date = str(start)
date = dt.datetime(int(date[0:4]), int(date[4:6]),
int(date[6:8])).date()
for i in range(0, size):
if (size == 1):
try:
avgTa = rDD['response']['body']['items']['item']['avgTa']
# avgTa 평균 기온
except TypeError:
avgTa = -99
try:
minTa = rDD['response']['body']['items']['item']['minTa']
# minTa 최저 기온
except TypeError:
minTa = -99
try:
maxTa = rDD['response']['body']['items']['item']['maxTa']
# maxTa 최고 기온
except TypeError:
maxTa = -99
try:
avgTd = rDD['response']['body']['items']['item']['avgTd']
# avgTd 평균 이슬점온도
except TypeError:
avgTd = -99
try:
minRhm = rDD['response']['body']['items']['item']['minRhm']
# minRhm 최소 상대습도
except TypeError:
minRhm = -99
try:
avgRhm = rDD['response']['body']['items']['item']['avgRhm']
# avgRhm 평균 상대습도
except TypeError:
avgRhm = -99
try:
ssDur = rDD['response']['body']['items']['item']['ssDur']
# ssDur 가조시간
except TypeError:
ssDur = -99
try:
sumSsHr = rDD['response']['body']['items']['item'][
'sumSsHr']
# sumSsHr 합계 일조 시간
except TypeError:
sumSsHr = -99
try:
hr1MaxIcsr = rDD['response']['body']['items']['item'][
'hr1MaxIcsr']
# hr1MaxIcsr 1시간 최다 일사량
except TypeError:
hr1MaxIcsr = -99
if (hr1MaxIcsr is None):
hr1MaxIcsr = -99
try:
sumGsr = rDD['response']['body']['items']['item']['sumGsr']
# sumGsr 합계 일사
except TypeError:
sumGsr = -99
if (sumGsr is None):
sumGsr = -99
try:
avgTca = rDD['response']['body']['items']['item']['avgTca']
# avgTca 평균 전운량
except TypeError:
avgTca = -99
try:
avgLmac = rDD['response']['body']['items']['item'][
'avgLmac']
# avgLmac 평균 중하층운량
except TypeError:
avgLmac = -99
try:
sumLrgEv = rDD['response']['body']['items']['item'][
'sumLrgEv']
# sumLrgEv 합계 대형증발량
except TypeError:
sumLrgEv = -99
if (sumLrgEv is None):
sumLrgEv = -99
try:
sumSmlEv = rDD['response']['body']['items']['item'][
'sumSmlEv']
# sumSmlEv 합계 소형증발량
except TypeError:
sumSmlEv = -99
if (sumSmlEv is None):
sumSmlEv = -99
try:
n99Rn = rDD['response']['body']['items']['item']['n99Rn']
# n99Rn 9-9 강수
except TypeError:
n99Rn = -99
if (n99Rn is None):
n99Rn = -99
try:
sumRn = rDD['response']['body']['items']['item']['sumRn']
# sumRn 일강수량
except TypeError:
sumRn = 0
if (sumRn is None):
sumRn = 0
if (float(sumRn) > 0):
sumRn = 1
else:
try:
avgTa = rDD['response']['body']['items']['item'][i][
'avgTa']
# avgTa 평균 기온
except TypeError:
avgTa = -99
try:
minTa = rDD['response']['body']['items']['item'][i][
'minTa']
# minTa 최저 기온
except TypeError:
minTa = -99
try:
maxTa = rDD['response']['body']['items']['item'][i][
'maxTa']
# maxTa 최고 기온
except TypeError:
maxTa = -99
try:
avgTd = rDD['response']['body']['items']['item'][i][
'avgTd']
# avgTd 평균 이슬점온도
except TypeError:
avgTd = -99
try:
minRhm = rDD['response']['body']['items']['item'][i][
'minRhm']
# minRhm 최소 상대습도
except TypeError:
minRhm = -99
try:
avgRhm = rDD['response']['body']['items']['item'][i][
'avgRhm']
# avgRhm 평균 상대습도
except TypeError:
avgRhm = -99
try:
ssDur = rDD['response']['body']['items']['item'][i][
'ssDur']
# ssDur 가조시간
except TypeError:
ssDur = -99
try:
sumSsHr = rDD['response']['body']['items']['item'][i][
'sumSsHr']
# sumSsHr 합계 일조 시간
except TypeError:
sumSsHr = -99
try:
hr1MaxIcsr = rDD['response']['body']['items']['item'][i][
'hr1MaxIcsr']
# hr1MaxIcsr 1시간 최다 일사량
except TypeError:
hr1MaxIcsr = -99
try:
sumGsr = rDD['response']['body']['items']['item'][i][
'sumGsr']
# sumGsr 합계 일사
except TypeError:
sumGsr = -99
try:
avgTca = rDD['response']['body']['items']['item'][i][
'avgTca']
# avgTca 평균 전운량
except TypeError:
avgTca = -99
try:
avgLmac = rDD['response']['body']['items']['item'][i][
'avgLmac']
# avgLmac 평균 중하층운량
except TypeError:
avgLmac = -99
try:
sumLrgEv = rDD['response']['body']['items']['item'][i][
'sumLrgEv']
# sumLrgEv 합계 대형증발량
except TypeError:
sumLrgEv = -99
try:
sumSmlEv = rDD['response']['body']['items']['item'][i][
'sumSmlEv']
# sumSmlEv 합계 소형증발량
except TypeError:
sumSmlEv = -99
try:
n99Rn = rDD['response']['body']['items']['item'][i][
'n99Rn']
# n99Rn 9-9 강수
except TypeError:
n99Rn = -99
try:
sumRn = rDD['response']['body']['items']['item'][i][
'sumRn']
# sumRn 일강수량
except TypeError:
sumRn = 0
if (sumRn is None):
sumRn = 0
if (float(sumRn) > 0):
sumRn = 1
weather[i] = [
str(date), avgTa, minTa, maxTa, avgTd, minRhm, avgRhm, ssDur,
sumSsHr, hr1MaxIcsr, sumGsr, avgTca, avgLmac, sumLrgEv,
sumSmlEv, n99Rn, heightList[index], sumRn
]
date = date + td.Timedelta(days=1)
return weather
def data_typhoon(start, end):
url = "http://apis.data.go.kr/1360000/AsosDalyInfoService/getWthrDataList" \
"?serviceKey=LAEin4h5h2HeNf9fuSWuorK2uW5MyuvoiWeJL3uSRZivdAzWhtcrCECKzSKrU9Dfwe8W6tdNR24tDTBZEPYiEQ%3D%3D" \
"&numOfRows=999" \
"&dataCd=ASOS" \
"&dateCd=DAY" \
"&startDt=" + str(start) + \
"&endDt=" + str(end) + \
"&stnIds=184"
request = ul.Request(url)
response = ul.urlopen(request)
rescode = response.getcode()
weather = [[]]
if (rescode == 200):
responseData = response.read()
rD = xmltodict.parse(responseData)
rDJ = json.dumps(rD)
rDD = json.loads(rDJ)
try:
size = int(rDD['response']['body']['totalCount'])
except KeyError:
size = 1
weather = [[0] * 8] * (size)
date = str(start)
date = dt.datetime(int(date[0:4]), int(date[4:6]),
int(date[6:8])).date()
for index in range(0, size):
try:
if (size == 1):
avgRhm = rDD['response']['body']['items']['item']['avgRhm']
else:
avgRhm = rDD['response']['body']['items']['item'][index][
'avgRhm']
# 평균 상대 습도
if avgRhm is None:
avgRhm = 0
except (TypeError, ValueError):
avgRhm = 0
try:
if (size == 1):
avgPs = rDD['response']['body']['items']['item']['avgPs']
else:
avgPs = rDD['response']['body']['items']['item'][index][
'avgPs']
# 평균 해면 기압
if avgPs is None:
avgPs = 0
except (TypeError, ValueError):
avgPs = 0
try:
if (size == 1):
avgPa = rDD['response']['body']['items']['item']['avgPa']
else:
avgPa = rDD['response']['body']['items']['item'][index][
'avgPa']
# 평균 현지 기압
if avgPa is None:
avgPa = 0
except (TypeError, ValueError):
avgPa = 0
try:
if (size == 1):
avgWs = rDD['response']['body']['items']['item']['avgWs']
else:
avgWs = rDD['response']['body']['items']['item'][index][
'avgWs']
# 평균 풍속
if avgWs is None:
avgWs = 0
except (TypeError, ValueError):
avgWs = 0
try:
if (size == 1):
sumRn = rDD['response']['body']['items']['item']['sumRn']
else:
sumRn = rDD['response']['body']['items']['item'][index][
'sumRn']
# 강수량
if sumRn is None:
sumRn = 0
except (TypeError, ValueError):
sumRn = 0
try:
if (size == 1):
avgTa = rDD['response']['body']['items']['item']['avgTa']
else:
avgTa = rDD['response']['body']['items']['item'][index][
'avgTa']
# 평균 기온
if avgTa is None:
avgTa = 0
except (TypeError, ValueError):
avgTa = 0
weather[index] = [
str(date), avgRhm, avgPs, avgPa, avgWs, sumRn, avgTa, 0
]
date = date + td.Timedelta(days=1)
return weather
import GoogleAnalytics
import datetime
import timedelta
import pandas as pd
# import Helper.Data as dt
import configparser
config = configparser.ConfigParser()
config.read('reporting_config.ini')
today = datetime.datetime.now().date()
yesterday = today - timedelta.Timedelta(days=1)
########
# ok - Get analytics
### sprint 1 Google Ads
### sprint 2 Facebook Ads
### sprint 3 Criteo
### sprint 4 output excel'i duzenle
### sprint 5 ali
# ok - out to excel
#########
# Get analytics
day_report = GoogleAnalytics.get_google_analytics_day_report(yesterday)
# rows, columns = day_report.shape
# print(rows, columns)