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obj_scheduling_tool.py
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import pandas as pd
import numpy as np
from functools import reduce
class Scheduler():
def __init__(self, _no_of_companies, _companies, _time_slots_companies, _panel_no_companies, _slots_companies) -> None:
self.no_of_companies = _no_of_companies
self.companies = _companies
self.time_slots_companies = _time_slots_companies
self.panel_no_companies = _panel_no_companies
self.slots_companies = _slots_companies
self.companies_df = []
self.possible_combinations = []
def read_data(self):
for company in self.companies:
self.companies_df.append(pd.read_csv(f"{company}.csv"))
def get_companiesdf(self):
return self.companies_df
def uppercase_names(self):
i = 0
for company_df in self.companies_df:
company_df['Name'] = company_df['Name'].str.upper()
i+=1
def change_column_name(self):
i = 0
for company_df in self.companies_df:
company_df.rename(columns = {'Status': f'{self.companies[i]}_Status'}, inplace = True)
i+=1
def merge_df(self):
df_custom = reduce(lambda left,right: pd.merge(left,right,on=["Name","Email ID"],
how='outer'), self.companies_df)
df_custom = df_custom.drop_duplicates()
self.df_final = df_custom
def fill_na(self):
self.df_final = self.df_final.replace(np.nan, 0)
def assign_PR(self):
self.df_final['PR'] = 0
for i in range(0,len(self.companies)):
self.df_final['PR'] = self.df_final['PR']+self.df_final[f'{self.companies[i]}_Status']
def sort_df(self):
self.df_final = self.df_final.sort_values(
by="PR",
ascending=False
)
self.df_final = self.df_final.drop(columns=['PR'])
def reset_index(self):
self.df_final.reset_index(drop=True, inplace=True)
def generate_combinations(self):
for k in range(0,self.no_of_companies):
(x,y) = (self.time_slots_companies[k], self.panel_no_companies[k])
self.possible_combinations.append([[[n,m,0] for m in range(1,y+1)] for n in range(x,x+self.slots_companies[k])])
def create_cols(self):
for i in range(0,len(self.companies)):
self.df_final[f'{self.companies[i]}_ts'] = np.nan
self.df_final[f'{self.companies[i]}_panel'] = np.nan
def allot_ts(self):
# Iterating through all the rows in the dataframe
for index, row in self.df_final.iterrows():
# Non_iter represents the timeslot in which the candidate is busy
non_iter = []
# k represents the company index
k=0
for itr in range(0,len(self.companies)):
# To check whether candidate has been shortlisted for Qualcomm
if row[f'{self.companies[itr]}_Status'] != 0:
breakout_flag = False
for i in range(len(self.possible_combinations[k])):
# Check whether candidate is busy during this time period, if yes
# continue to the next iteration
if i in non_iter:
continue
for j in range(len(self.possible_combinations[k][0])):
# Check for the first panel which is free
if self.possible_combinations[k][i][j][2]==0:
# Allot the timeslot and panel no
self.df_final.at[index,f'{self.companies[itr]}_ts'] = self.possible_combinations[k][i][j][0]
self.df_final.at[index,f'{self.companies[itr]}_panel']= self.possible_combinations[k][i][j][1]
# Change the status of the timeslot and panel no combination
self.possible_combinations[k][i][j][2] = 1
# Make this timeslot unavailble for the next companies for the candidate
non_iter.append(self.possible_combinations[k][i][j][0])
# To break out from the outer loop
breakout_flag = True
# To break out from the inner loop
break
if breakout_flag:
break
if row[f'{self.companies[itr]}_ts']==np.nan:
print("Scheduler not working!")
k=k+1
def change_col(self):
for itr in range(0,len(self.companies)):
self.df_final.loc[self.df_final[f'{self.companies[itr]}_Status'] == 1, f'{self.companies[itr]}_Status'] = 'Shortlisted'
def change_ts(self):
times = ['9:00-9:45', '9:45-10:30', '10:30-11:15', '11:15-12:00', '12:00-12:45', '12:45-1:30', '1:30-2:15', '2:15-3:00']
df = self.df_final
for index, row in df.iterrows():
for company in self.companies:
if row[f'{company}_Status'] == 'Shortlisted':
itr_float = row[f'{company}_ts']
itr = np.int16(itr_float).item()
# itr = df.at[index,f'{company}_ts']
# print(itr)
df.loc[index, f'{company}_ts'] = times[itr]
self.df_final= df
def change_na(self):
def convert_na(val):
if val==0:
return np.nan
else:
return val
for itr in range(0,len(self.companies)):
self.df_final[f'{self.companies[itr]}_Status'] = self.df_final[f'{self.companies[itr]}_Status'].apply(convert_na)
def get_df(self):
return self.df_final
def get_schedule(self):
self.read_data()
self.uppercase_names()
self.change_column_name()
self.merge_df()
self.fill_na()
self.assign_PR()
self.sort_df()
self.reset_index()
self.generate_combinations()
self.create_cols()
self.allot_ts()
self.change_col()
self.change_ts()
self.change_na()
df = self.get_df()
return df
def get_csv(self):
df = self.get_df
csv_file = df.to_csv('final_ET_schedule.csv')
return csv_file