Hi,
I have made the complete program but when I am compiling the program it is
showing errors. Can you please help to resolve this?
The code is in the file attached with this mail.
On Fri, Jul 5, 2019 at 10:44 PM Animesh Bhadra <animeshb.soc...@gmail.com>
wrote:
> Thanks Alan and Mats for the explanation.
>
> On 05/07/19 19:57, Mats Wichmann wrote:
> > On 7/4/19 3:53 PM, Alan Gauld via Tutor wrote:
> >
> >>> Does this means that the Dict is ordered? or it is implementation
> dependent?
> >> Neither, it means the items in a list always have indexes
> >> starting at zero.
> >>
> >> By pure coincidence dictionaries in recent Python versions (since 3.6
> >> or 3.7???) retain their insertion order. But that was not always the
> >> case, but the result would have been the same so far as the 0,1,2 bit
> goes.
> >>
> > To be a little more precise, in 3.6 CPython insertion order was
> > preserved as an artefact of the new implementation of dicts, but not
> > promised to be that way. Since 3.7 it is guaranteed (it is actually in
> > the language specification, so other Pythons have to do this too now).
> >
> > It's still not the same as a collections.OrderedDict, which has some
> > useful additional features in case you care a lot about ordering.
> >
> > _______________________________________________
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>
import math
import csv
import pdb
import pandas as pd
import numpy as np
from math import radians, sin, cos, acos
def distanceCalculator(latitude1,longitude1,latitude2,longitude2):
slat = radians(latitude1)
slon = radians(longitude1)
elat = radians(latitude2)
elon = radians(longitude2)
dist = 6371.01 * acos(sin(slat)*sin(elat) +
cos(slat)*cos(elat)*cos(slon - elon))
return dist
df = pd.read_csv("venueData.csv",header=None)
df.columns=['name','latitude','longitude','district','block']
df2 = pd.read_csv("mtdata.csv",header=None)
df2.columns = ['name','location','latitude','longitude','subject']
teacher = pd.read_csv("teachers.csv")
teacher.head()
df.head()
teacher['Latitude'] = teacher['Latitude'].apply(lambda x: x.rstrip(",") if
type(x) == str else x )
teacher['Longitude'] = teacher['Longitude'].apply(lambda x: x.rstrip(",") if
type(x) == str else x )
listEmpty = []
dictionaryTeacher = {}
for i,ex in teacher.iterrows():
lat1 = ex['Latitude']
lon1 = ex['Longitude']
Id = i
for b,c in df.iterrows():
lat2 = c['latitude']
lon2 = c['longitude']
nameVen = c['name']
listEmpty.append((distanceCalculator(float(lat1),float(lon1),float(lat2),float(lon2)),Id,b))
demian = []
listEmpty.sort()
demian = listEmpty[0]
dictionaryTeacher[ex['Name']] = demian
listEmpty = []
demian = []
DataTeacher =
pd.DataFrame(columns=['Teacher','Distance','Venue','Eng','Hindi','Maths','TeacherId'])
number = 3
for ex in dictionaryTeacher:
DataTeacher=
DataTeacher.append({'Teacher':ex,'Distance':dictionaryTeacher[ex][0],'Venue':df.loc[dictionaryTeacher[ex][2]]['name'],'Eng':teacher.loc[dictionaryTeacher[ex][1]]['Eng'],'Hindi':teacher.loc[dictionaryTeacher[ex][1]]['Hindi'],'Maths':teacher.loc[dictionaryTeacher[ex][1]]['Maths'],'TeacherId':dictionaryTeacher[ex][1]},ignore_index=True)
days = pd.read_csv("days.csv")
days.columns = ['January', 'January:Days', 'February', 'Feburary:Days', 'March',
'March:Days', 'April', 'April:Days', 'May', 'May:Days', 'June',
'June:Days', 'July', 'July:Days', 'August', 'August:Days',
'September', 'September:Days', 'October', 'October:Days', 'November',
'November:Days', 'December', 'December:Days']
df.columns=['name','latitude','longitude','district','block']
df['name'] = df['name'].apply(lambda x: x.rstrip())
df2['name'] =df2['name'].apply(lambda x: x.rstrip())
venue = {}
for i,k in enumerate(df['name']):
if (k not in venue):
venue[k] = {'January': 0 ,'February':0 , 'March': 0 , 'April':0,
'May':0,'June ':0 ,'July':0 ,
'August':0,'September':0,'October':0,'November':0,'December':0}
teacher = {}
for i,k in enumerate(df2['name']):
if (k not in teacher):
teacher[k] = {'January': 0 ,'February':0 , 'March': 0 , 'April':0,
'May':0,'June ':0 ,'July':0 ,
'August':0,'September':0,'October':0,'November':0,'December':0}
teacher['Ajmal']
dictionary = {}
liste = []
for i,ex in df2.iterrows():
nameT = ex['name']
lat1 = ex['latitude']
lon1 = ex['longitude']
sub = ex['subject']
Id = i
for b , x in df.iterrows():
nameM = x['name']
lat2 = x['latitude']
lon2 = x['longitude']
id2 = b
liste.append((distanceCalculator(lat1,lon1,lat2,lon2),i,id2))
liste.sort()
dictionary[ex['name']] = liste[0:3]
liste = []
Data =
pd.DataFrame(columns=['Trainer','Venue','Distance','Subjects','Location','VenDistrict','VenBlocks'])
for ex in dictionary:
for i , k in enumerate(dictionary[ex]):
Data = Data.append({'Trainer':ex ,'Venue':
df.loc[dictionary[ex][i][2]]['name'],'Distance':
dictionary[ex][i][0],'Subjects':df2.loc[dictionary[ex][i][1]]['subject'],'Location':df2.loc[dictionary[ex][i][1]]['location'],'VenDistrict':df.loc[dictionary[ex][i][2]]['district'],'VenBlocks':df.loc[dictionary[ex][i][2]]['block']},ignore_index=True)
teacher['Akalmash']
Data['Month'] = -1
for i,ex in Data.iterrows():
Train = ex['Trainer']
Venue = ex['Venue']
for ex in teacher[Train]:
if(teacher[Train][ex]==0):
if(venue[Venue][ex] == 0):
teacher[Train][ex] = 1
venue[Venue][ex] = 1
Data.loc[i,"Month"] = ex
break
days[days["January"][:].str.contains("Tue")]['January'].index
strings = " "
for ex in days[days["January"][:].str.contains("Tue")]['January'].index:
strings += ","+ str(ex)
strings = strings.rstrip(",")
strings = strings.lstrip()
#days = days.apply(lambda x : x.rstrip())
Data['Month'] = Data['Month'].apply(lambda x : x.rstrip())
listeDays = []
Cal = 0
num = 0
strings = ""
for i,ex in Data.iterrows():
ay = ex['Month']
indexOfDays = days[days[ay][:].str.contains("Mon") |
days[ay][:].str.contains("Tue") | days[ay][:].str.contains("Wed") |
days[ay][:].str.contains("Thu") | days[ay][:].str.contains("Fri")][ay].index
for ex in days[days[ay][:].str.contains("Mon") |
days[ay][:].str.contains("Tue") | days[ay][:].str.contains("Wed") |
days[ay][:].str.contains("Thu") | days[ay][:].str.contains("Fri")][ay]:
num = indexOfDays[Cal]
num +=1
Cal +=1
strings += str(num) + "," + ex + ","
Data.loc[i,"Days"] = strings
num = 0
Cal = 0
strings = ""
Data['Month'] = Data['Month'].apply(lambda x: x.rstrip())
Data['Days'] = Data['Days'].apply(lambda x : x.replace("\n",","))
#Data.drop("Month",axis=1,inplace=True)
DfSub = pd.read_csv("backupMasterTrainers.csv")
copyDfSub = DfSub
ab = DfSub.sample(n=1)
ab.columns = ['Name',"Location","Latitude","Longitude","Subject"]
#ab.loc[10,"Name"]
#Data.columns
Id = ""
VenID = 0
newName = ""
dist = ""
VenLat =""
VenLot =""
newLat = ""
newLon = ""
newSub = ""
newLoc = ""
for i,ex in Data.iterrows():
name = ex['Trainer']
month = ex['Month']
lecture = ex['Subjects']
print(name,"Do you want to give ",lecture,"in this month :",month)
answer = input("For Yes : Y For No N")
if answer == 'Y' or answer == 'y':
pass
elif answer == 'N' or answer =='n':
ab = copyDfSub.sample(n=1)
ab.columns = ['Name',"Location","Latitude","Longitude","Subject"]
Id = ab.index[0]
newName = ab.loc[Id,"Name"]
newLat = ab.loc[Id,"Latitude"]
newLon = ab.loc[Id,"Longitude"]
newSub = ab.loc[Id,"Subject"]
newLoc = ab.loc[Id,"Location"]
VenueName = ex['Venue']
for z,b in df.iterrows():
if(VenueName in b['name']):
VenID = z
VenLat = ven['Latitude'][z]
VenLon = ven['Longitude'][z]
dist = distanceCalculator(newLat,newLon,VenLat,VenLon)
Data.loc[i,"Trainer"] = newName
Data.loc[i,"Distance"] = dist
Data.loc[i,"Subjects"] = newSub
Data.loc[i,"Location"] = newLoc
copyDfSub.drop(Id,inplace=True)
copyDfSub.reset_index(drop=True,inplace=True)
df5 = pd.merge(Data,DataTeacher,on="Venue",how='inner')
df5.groupby("Teacher")
teachers = pd.read_csv("teachers.csv")
teachers.groupby(['Name','Location'],as_index=False).agg({'Distance_x':
'count'})
TeachDictMon = {}
for i,ex in teachers.iterrows():
NameTeach = ex['Name']
if NameTeach not in TeachDictMon:
TeachDictMon[NameTeach] =
{'January':0,'February':0,'March':0,'April':0,'May':0,'June':0,'July':0,'August':0,'September':0,'October':0,'November':0,'December':0}
TeachDictMon
df5= pd.merge(Data,DataTeacher,on='Venue',how='inner')
df5['Subjects'] = df5['Subjects'].apply(lambda x : x.title())
df5.columns = ['Trainer', 'Venue', 'Distance_x', 'Subjects', 'Location',
'VenDistrict',
'VenBlocks', 'Month', 'Days', 'Teacher', 'Distance_y', 'Eng', 'Hindi',
'Math', 'TeacherId']
df5.head()
dfNew =
pd.DataFrame(columns=["Venue","Batches","Month","Block","District","Subjects","Date"])
dictBat = {}
BatchPoint = 0
for i,ex in df5.iterrows():
teacherId = ex['TeacherId']
train = ex['Trainer']
ven = ex['Venue']
sub = ex['Subjects']
month = ex['Month']
a = df5[(df5['Trainer']==train) & (df5['Venue'] == ven) & (df5['Subjects']
== sub)]
con = a.count()
BatchPoint = len(con)
for c,b in a.iterrows():
teacherId = b['TeacherId']
train = b['Trainer']
ven = b['Venue']
suber = b['Subjects']
sub = a.loc[c][suber]
if(sub not in dictBat):
dictBat[sub] = []
dictBat[sub].append(teacherId)
else:
dictBat[sub].append(teacherId)
dfNew =
dfNew.append({'Venue':ven,'Batches':dictBat,'Month':month,"District":ex['VenDistrict'],'Subjects':ex['Subjects'],'Blocks':ex['VenBlocks'],'Date':ex['Days']},ignore_index=True)
dictBat = {}
df5.drop(a.index,inplace=True)
a = dfNew[dfNew['Batches'] == {}].index
dfNew.head()
dfNew.drop("Block",axis=1,inplace=True)
dfNew.drop(a,inplace=True)
dfNew.reset_index(drop=True,inplace=True)
dfNew.head()
len(dfNew["Batches"][0]['G1'])
list(dfNew.loc[0,"Batches"].keys())[0]
Output = pd.DataFrame(columns=['S', 'Date', 'Subject', 'Group', 'Trained',
'District', 'Block',
'Venue', 'Month'])
GroupName = ""
string = ""
num = 0
count = 0
for i,ex in dfNew.iterrows():
Ven = ex['Venue']
Batch = ex['Batches']
Month = ex['Month']
Block = ex['Blocks']
Dist = ex['District']
Sub = ex['Subjects']
Date = ex['Date']
num = len(Batch)
for x in range(num):
if x == 0:
trained = len(Batch[list(dfNew.loc[i,"Batches"].keys())[x]])
string = Date.split(",")
s = str(list(dfNew.loc[i,"Batches"].keys())[x])
s= s[1]
Output=Output.append({'S':s,'Date':str(string[x:x+4]),'Subject':Sub,'Group':list(dfNew.loc[i,"Batches"].keys())[x],'Trained':trained,'District':Dist,'Block':Block,'Venue':Ven,'Month':Month},ignore_index=True)
else:
trained = len(Batch[list(dfNew.loc[i,"Batches"].keys())[x]])
s = str(list(dfNew.loc[i,"Batches"].keys())[x])
s = s[1]
Output=Output.append({'S':s,'Date':str(string[x*4:x*4+4]),'Subject':Sub,'Group':list(dfNew.loc[i,"Batches"].keys())[x],'Trained':trained,'District':Dist,'Block':Block,'Venue':Ven,'Month':Month},ignore_index=True)
Output
Td=TeachDictMon
Output = pd.DataFrame(columns =
["S","Date","Subject","Group","Trained","District","Block","Venue","Month"])
Output
df5['Subjects'] = df5['Subjects'].apply(lambda x : x.title())
a=df5[(df5['Subjects'] == 'Eng') & (df5['Venue'] == 'Gobind pura')]
numOfMonth = 0
for i,ex in df5.iterrows():
TrainerName = ex['Trainer']
Venue = ex['Venue']
VenBlock = ex['VenBlocks']
Month = ex['Month']
df5.columns = ['Trainer', 'Venue', 'Distance_x', 'Subjects', 'Location',
'VenDistrict',
'VenBlocks', 'Month', 'Days', 'Teacher', 'Distance_y', 'Eng', 'Hindi',
'Math', 'TeacherId']
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