locationIntelligenceModel/header.py at master · chaffeechenyefei/locationIntelligenceModel · GitHub

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import pandas as pd
import numpy as np
import datetime as datetime
import os

pj = os.path.join

citynameabbr = ['PA', 'SF', 'SJ', 'LA', 'NY']

cityname = ['Palo Alto', 'San Francisco', 'San Jose', 'Los Angeles', 'New York']

cfile = ['dnb_pa.csv', 'dnb_sf.csv', 'dnb_sj.csv', 'dnb_Los_Angeles.csv', 'dnb_New_York.csv']

class data_process(object):
    def __init__(self, root_path, silence=False):
        self.root_path = root_path
        self.demand_col = {
            'acc_col': 'Organization_Account__c',
            'req_desk': 'Desired_Desks__c',
            'date_col': 'LastModifiedDate',
            'move_in_date': 'Desired_Move_In_Date__c',
        }
        self.inv_col = {
            'bid': 'atlas_location_uuid',
            'date_col': 'report_month',  # 2020/1/1 0:00
            'cap': 'max_reservable_capacity',
            'occ': 'occupancy_rate',  # 0-100
        }
        self.opp_col = {
            'bid': 'atlas_location_uuid',
            'cid': 'AccountId',
            'date_col': 'date',
            'status_col': 'status',
            'n_status_col': 'n_status',  # 'Open/Close'
        }
        self.talent_col = {
            'city': 'city',
            'state': 'state',
            'talent_score': 'talent_index',  # 5-1
        }

        self.sil = silence
        self.demand_dat = 'salesforce demand signal'
        self.ac_dat = 'salesforce ac_id'
        self.inv_dat = 'inventory data'
        self.op_dat = 'opportunity table'
        self.talent_dat = 'talent table'

    def load_inventory(self, db='compstak', dbname='inventory_bom.csv'):
        db_path = pj(self.root_path, db)
        bid = self.inv_col['bid']
        date_col = self.inv_col['date_col']

        inv_dat = pd.read_csv(pj(db_path, dbname))

        inv_dat = inv_dat.sort_values([bid, date_col]) \
            .drop_duplicates([bid], keep='last')

        self.inv_dat = inv_dat
        if not self.sil:
            print('%d atlas loaded' % len(inv_dat))

    def load_talent(self, db='talent', dbname='talent_score_v0_01-03-2020.csv'):
        db_path = pj(self.root_path, db)

        city_col = self.talent_col['city']
        talent_score = self.talent_col['talent_score']
        state_col = self.talent_col['state']

        tal_dat = pd.read_csv(pj(db_path, dbname), index_col=0)[[city_col, state_col, talent_score]]

        # check dup
        uniq_num = len(tal_dat.groupby([city_col, state_col]).first())
        assert (uniq_num == len(tal_dat))

        self.talent_dat = tal_dat
        if not self.sil:
            print('%d talent loaded' % len(tal_dat))
        return self.talent_dat

    def load_opportunity(self, db='salesforce', dbname='opportunities.csv', save_dbname='salesforce_pair.csv'):
        db_path = pj(self.root_path, db)
        cid = self.opp_col['cid']
        bid = self.opp_col['bid']
        date_col = self.opp_col['date_col']
        status_col = self.opp_col['status_col']
        n_status_col = self.opp_col['n_status_col']
        kset = set(['closed', 'close', 'closing'])

        op_dat = pd.read_csv(pj(db_path, dbname), header=None,
                             names=['Id', cid, bid, status_col, date_col, 'n2', 'n3', 'n4'])
        op_dat[[date_col]] = op_dat[[date_col]].fillna('1990-01-01')
        op_dat = op_dat.sort_values([cid, bid, date_col]).drop_duplicates([cid, bid], keep='last')
        op_dat[[status_col]] = op_dat[[status_col]].fillna('Closed')
        op_dat[n_status_col] = op_dat[status_col].apply(
            lambda x: 'Closed' if kset & set(x.lower().split(' ')) else 'Open')
        self.op_dat = op_dat
        if not self.sil:
            print('%d latest opp loaded' % len(op_dat))
        self.op_dat.to_csv(pj(db_path, save_dbname))

    def load_account(self, db='salesforce', dbname='accounts.csv'):
        db_path = pj(self.root_path, db)
        ac_dat = pd.read_csv(pj(db_path, dbname), error_bad_lines=False, header=None)
        ac_dat = ac_dat[[8, 9]]
        ac_dat = ac_dat.rename(columns={8: 'AccountId', 9: 'Name'})
        self.ac_dat = ac_dat
        if not self.sil:
            print('%d accounts loaded' % len(ac_dat))

    def load_demand(self, db='salesforce', dbname='demand_signals_191110.csv'):
        db_path = pj(self.root_path, db)
        acc_col = self.demand_col['acc_col']
        req_desk = self.demand_col['req_desk']
        date_col = self.demand_col['date_col']
        move_in_date = self.demand_col['move_in_date']  # 2022-01-01T00:00:00Z

        self.demand_dat = pd.read_csv(pj(db_path, dbname))[[acc_col, req_desk, date_col, move_in_date]]
        self.demand_dat[[move_in_date]] = self.demand_dat[[move_in_date]].fillna('1990-01-01')
        self.demand_dat[move_in_date] = self.demand_dat[move_in_date].apply(lambda x: x[:10])  # "%Y-%m-%d"

        if not self.sil:
            print('%d demands signal loaded' % len(self.demand_dat))

    def deduplicate_demand_tb(self, db='salesforce', save_dbname='demand_deduplicate.csv'):
        db_path = pj(self.root_path, db)
        acc_col = self.demand_col['acc_col']
        req_desk = self.demand_col['req_desk']
        date_col = self.demand_col['date_col']
        move_in_date = self.demand_col['move_in_date']

        de_demand_dat = self.demand_dat[[acc_col, req_desk, date_col, move_in_date]]

        def trans_time4sort(x):
            """
            2019-06-19T19:40:55Z => 20190619
            """
            x = str(x)
            x = x.replace('-', '')
            x = x[:8]
            return x

        de_demand_dat['time'] = de_demand_dat[date_col].apply(lambda x: trans_time4sort(x))
        de_demand_dat = de_demand_dat.sort_values([acc_col, 'time']).drop_duplicates([acc_col], keep='last')
        if not self.sil:
            print('%d unique acc demands remains' % len(de_demand_dat))
        de_demand_dat.to_csv(pj(db_path, save_dbname))

    def load_location_scorecard_msa(self, db='', dbname='location_scorecard_200106.csv'):
        db_path = pj(self.root_path, db)
        bid = self.ls_col['bid']
        state_col = self.ls_col['state']
        city_col = self.ls_col['city']

        lsdat = pd.read_csv(pj(db_path, dbname), index_col=0)[[bid, state_col, city_col]]
        if not self.sil:
            print('%d location scorecard for msa loaded' % len(lsdat))
        return lsdat

    def load_dnb_city_lst(self, db='reason_table', table='dnb_city_list_200106.csv'):
        db_path = pj(self.root_path, db)
        dnb_city_lst = pd.read_csv(pj(db_path, table), index_col=0)

        citylongname = list(dnb_city_lst['physical_city'].values)
        cityabbr = list(dnb_city_lst['short_name'].values)
        origin_comp_file = list(dnb_city_lst['filename'].values)

        return {
            "citylongname":citylongname,
            "cityabbr":cityabbr,
            "origin_comp_file":origin_comp_file,
        }