{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "4b2cc7f7", "metadata": {}, "outputs": [], "source": [ "import unicodedata\n", "import pandas as pd\n", "import numpy as np\n", "from faker import Faker\n", "import random\n", "import itertools\n", "\n", "random.seed(10)\n", "\n", "fake = Faker()\n", "\n", "N_OWNER = 2500\n", "N_EMPLOYEE = 60\n", "N_COMPANY = N_OWNER * int(np.random.ranf() + 1)\n", "\n", "\n", "def remove_accents(input_str):\n", " nfkd_form = unicodedata.normalize('NFKD', input_str)\n", " only_ascii = nfkd_form.encode('ASCII', 'ignore')\n", " return only_ascii.decode()\n", "\n", "# 1. Industry table\n", "industry_df = pd.DataFrame({\n", " 'industry_name': ['medical supplies', 'logistics', 'construction']\n", "})\n", "\n", "def generate_random_date():\n", " # Generate a random year between 2012 and 2020\n", " year = np.random.randint(2012, 2021)\n", " # Generate a random month between 1 and 12\n", " month = np.random.randint(1, 13)\n", " # Generate a random day between 1 and 28\n", " day = np.random.randint(1, 29)\n", " # Format the date as a string\n", " random_date = f\"{year:04d}-{month:02d}-{day:02d}\"\n", " return random_date\n", "\n", "# 2. Owner table\n", "def generate_vietnamese_name():\n", " with open(\"last_name.txt\", \"r\") as f:\n", " last_names = [i.strip() for i in (f.readlines())]\n", " with open(\"middle_name.txt\", \"r\") as f:\n", " middle_names = [i.strip() for i in (f.readlines())]\n", " with open(\"first_name.txt\", \"r\") as f:\n", " first_names = [i.strip() for i in (f.readlines())]\n", " return f\"{random.choice(last_names)} {random.choice(middle_names)} {random.choice(first_names)}\"\n", "\n", "owner_df = pd.DataFrame({\n", " 'owner_cmnd': [f\"0300{fake.unique.random_number(digits=8, fix_len=True)}\" for _ in range(N_OWNER)],\n", " 'owner_name': [generate_vietnamese_name() for _ in range(N_OWNER)],\n", " 'owner_sdt': [f\"+84{fake.unique.random_number(digits=9, fix_len=True)}\" for _ in range(N_OWNER)]\n", "})\n", "\n", "owner_df['owner_email'] = owner_df['owner_name'].apply(remove_accents).str.replace(' ', '').str.lower() + owner_df['owner_cmnd'] + '@gmail.com'\n", "\n", "\n", "# 3. Employee table\n", "employee_codes = [f\"emp-{i}\" for i in range(N_EMPLOYEE)]\n", "employee_names = [fake.unique.first_name() for _ in range(N_EMPLOYEE)]\n", "# areas = ['Hà Nội', 'Hải Phòng', 'Hồ Chí Minh', 'Quy Nhơn', 'Đà Nẵng', 'Khánh Hòa', 'Kiên Giang', 'Cần Thơ', 'Bình Thuận', 'Nghệ An', 'Hà Tĩnh', \"Quảng Ninh\", \"Điện Biên\", \"Cao Bắng\", \"Thanh Hoá\", \"Ninh Thuận\"]\n", "areas = pd.read_csv(\"provinces.txt\", header=None)[0]" ] }, { "cell_type": "code", "execution_count": 2, "id": "0f8e5bb2", "metadata": {}, "outputs": [], "source": [ "employee_df = pd.DataFrame({\n", " 'empl_persnbr': random.sample(employee_codes, N_EMPLOYEE),\n", " 'empl_name': employee_names,\n", " 'area': [random.choice(areas) for _ in range(N_EMPLOYEE)],\n", " 'title': [random.choice(['title1', 'title2', 'title3']) for _ in range(N_EMPLOYEE)]\n", "})\n", "\n", "employee_df['branch'] = employee_df['area'] + '_' + pd.Series(np.random.randint(1,4, N_EMPLOYEE)).astype(str)\n" ] }, { "cell_type": "code", "execution_count": 3, "id": "e8f02beb", "metadata": {}, "outputs": [], "source": [ "# 4. Company table\n", "def generate_org_name():\n", " letters = ''.join(random.choices('ABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789', k=5))\n", " suffix = random.choice(['JSC', 'LTD', 'CORP', 'GROUP', 'COMP', 'AND PARTNERS', 'ORG', 'HANOI', 'HCM'])\n", " scope = random.choice(['International', 'National', 'Global', 'Universal', 'Continental', \"And Partners\", \"FAMILY\"])\n", " return f\"{letters} {suffix} {scope}\"\n", "\n", "\n", "company_df = pd.DataFrame({\n", " 'tax_id': [f\"{fake.unique.random_number(digits=10, fix_len=True)}\" for _ in range(N_COMPANY)],\n", " 'owner_cmnd': np.random.choice(owner_df['owner_cmnd'], N_COMPANY),\n", " 'org_type': [\"KHMOI\" if random.random() <= .8 else \"KHHH\" for _ in range(N_COMPANY)],\n", " 'nganh_trong_tam': np.random.choice(industry_df['industry_name'], N_COMPANY),\n", " 'mnsegmtcd': np.random.choice(['SME', 'MMLC'], N_COMPANY),\n", " 'post_tel': [f\"+8420{fake.unique.random_number(digits=8, fix_len=True)}\" for _ in range(N_COMPANY)],\n", " 'ma_pers_nv': np.random.choice(employee_df['empl_persnbr'], N_COMPANY),\n", " 'addrname': [random.choice(areas) for _ in range(N_COMPANY)],\n", " 'gpkdngaycap': [generate_random_date() for _ in range(N_COMPANY)],\n", " \"tpktdesc\": [random.choice([\"Nhà nước\", \"Tập thể\", \"Tư nhân\", \"Có vốn đầu tư nước ngoài\"]) for _ in range(N_COMPANY)]\n", "})\n", "company_df['orgnbr'] = 'dn_' + company_df['tax_id']\n", "company_df['org_name'] = [generate_org_name() for _ in range(N_COMPANY)]\n", "company_df['post_mail'] = company_df['org_name'].str.lower().str.replace(' ', '') + [random.choice(['@gmail.com', '@yahoo.com']) for _ in range(N_COMPANY)]\n", "company_df['website'] = company_df['org_name'].str.lower().str.replace(' ', '') + [random.choice(['.com', '.net', '.org', '.com.vn', '.io', '.uk']) for _ in range(N_COMPANY)]\n", "company_df['start_date'] = company_df['gpkdngaycap']\n", "company_df['approach_status'] = company_df['org_type'].apply(lambda x: '-' if x=='KHHH' else np.random.choice(['fresh',\n", " 'scouted',\n", " 'scheduled',\n", " 'success',\n", " 'failure',\n", " ],\n", " p=[.6,.2,.05, .05, .1]))\n", "\n", "company_df['priority'] = company_df['org_type'].apply(lambda x: 3 if x=='KHHH' else (1 if random.random() <=.7 else 2))\n", "company_df['owner_cmnd'] = company_df.apply(lambda x: np.nan if x['priority']==2 else x['owner_cmnd'], axis=1)" ] }, { "cell_type": "code", "execution_count": 4, "id": "b5c95762", "metadata": {}, "outputs": [], "source": [ "import string\n", "\n", "def generate_company_data(N):\n", " data = {\n", " \"TOTAL_BALAMT\": np.random.randint(1_000_000_000, 10_000_000_000, N).tolist(),\n", " \"TOTAL_BALANCE\": np.random.randint(1_000_000_000, 10_000_000_000, N).tolist(),\n", " \"TONGTAISAN_2021\": np.random.randint(10_000_000_000, 100_000_000_000, N).tolist(),\n", " \"TONGTAISAN_2022\": [], # Will be the same as TONGTAISAN_2021\n", " \"TAISAN_NGANHAN_2021\": np.random.randint(1_000_000_000, 50_000_000_000, N).tolist(),\n", " \"TAISAN_NGANHAN_2022\": [], # Will be the same as TAISAN_NGANHAN_2021\n", " \"PHAITHU_2021\": np.random.randint(1_000_000_000, 20_000_000_000, N).tolist(),\n", " \"PHAITHU_2022\": [], # Will be the same as PHAITHU_2021\n", " \"TAISAN_DAIHAN_2021\": np.random.randint(1_000_000_000, 50_000_000_000, N).tolist(),\n", " \"TAISAN_DAIHAN_2022\": [], # Will be the same as TAISAN_DAIHAN_2021\n", " \"NO_DAIHAN_2021\": np.random.randint(1_000_000_000, 20_000_000_000, N).tolist(),\n", " \"NO_DAIHAN_2022\": [], # Will be the same as NO_DAIHAN_2021\n", " \"NO_NGANHAN_2021\": np.random.randint(1_000_000_000, 20_000_000_000, N).tolist(),\n", " \"NO_NGANHAN_2022\": [], # Will be the same as NO_NGANHAN_2021\n", " \"VONCHUSOHUU_2021\": np.random.randint(1_000_000_000, 20_000_000_000, N).tolist(),\n", " \"VONCHUSOHUU_2022\": [], # Will be the same as VONCHUSOHUU_2021\n", " \"HANGTONKHO_2021\": np.random.randint(1_000_000, 50_000_000, N).tolist(),\n", " \"HANGTONKHO_2022\": [], # Will be the same as HANGTONKHO_2021\n", " \"VAY_TCTD_KHAC\": [random.choice(['Y', 'N']) for _ in range(N)],\n", " \"CO_HD_XNK\": [random.choice(['Y', 'N']) for _ in range(N)],\n", " \"MAT_CAN_DOI_VON_2021\": [random.choice(['Y', 'N']) for _ in range(N)],\n", " \"MAT_CAN_DOI_VON_2022\": [], # Will be the same as MAT_CAN_DOI_VON_2021\n", " \"TY_SO_THANH_TOAN_2021\": np.random.uniform(0.5, 2.0, N).tolist(),\n", " \"TY_SO_THANH_TOAN_2022\": [], # Will be the same as TY_SO_THANH_TOAN_2021\n", " \"DOANH_THU_2021\": np.random.randint(1_000_000_000, 50_000_000_000, N).tolist(),\n", " \"DOANH_THU_2022\": [], # Will be the same as DOANH_THU_2021\n", " \"LOI_NHUAN_TRUOC_THUE_2021\": np.random.randint(100_000_000, 5_000_000_000, N).tolist(),\n", " \"LOI_NHUAN_TRUOC_THUE_2022\": [], # Will be the same as LOI_NHUAN_TRUOC_THUE_2021\n", " \"LOI_NHUAN_SAU_THUE_2021\": np.random.randint(50_000_000, 4_000_000_000, N).tolist(),\n", " \"LOI_NHUAN_SAU_THUE_2022\": [] # Will be the same as LOI_NHUAN_SAU_THUE_2021\n", " }\n", "\n", " # Set values for 2022 to be the same as 2021\n", " for key in [\"TONGTAISAN_2022\", \"TAISAN_NGANHAN_2022\", \"PHAITHU_2022\", \"TAISAN_DAIHAN_2022\", \"NO_DAIHAN_2022\",\n", " \"NO_NGANHAN_2022\", \"VONCHUSOHUU_2022\", \"HANGTONKHO_2022\", \"MAT_CAN_DOI_VON_2022\",\n", " \"TY_SO_THANH_TOAN_2022\", \"DOANH_THU_2022\", \"LOI_NHUAN_TRUOC_THUE_2022\", \"LOI_NHUAN_SAU_THUE_2022\"]:\n", " data[key] = data[key[:-5] + \"_2021\"]\n", "\n", " return data\n", "\n", "company_df_numeric = pd.DataFrame(generate_company_data(N_COMPANY))\n", "company_df = pd.concat([company_df, company_df_numeric], axis=1)" ] }, { "cell_type": "code", "execution_count": 5, "id": "39778564", "metadata": {}, "outputs": [], "source": [ "# 5. Ledger table\n", "num_ledger_rows = int(N_COMPANY * np.random.uniform(1.4, 1.8))\n", "tax_ids = company_df['tax_id'].tolist()\n", "ledger_pairs = list(itertools.permutations(tax_ids, 2))\n", "random.shuffle(ledger_pairs)\n", "ledger_pairs = ledger_pairs[:num_ledger_rows]\n", "\n", "ledger_df = pd.DataFrame({\n", " 'provider_tax_id': [pair[0] for pair in ledger_pairs],\n", " 'customer_tax_id': [pair[1] for pair in ledger_pairs],\n", " 'vat_number': np.random.randint(1, 11, num_ledger_rows),\n", " 'total_vat_amount': np.random.randint(5, 5001, num_ledger_rows) * 1000000\n", "})\n", "\n", "# 6. Bank table\n", "bank_df = pd.read_csv(\"./bank_name.txt\")\n", "bank_df['bank_name'] = bank_df['shortname']\n", "bank_df = bank_df[['bank_name']]" ] }, { "cell_type": "code", "execution_count": 6, "id": "7cafcad7", "metadata": {}, "outputs": [], "source": [ "# 7. CompBankBalance table\n", "comp_bank_balance_data = []\n", "for tax_id in company_df['tax_id']:\n", " num_accounts = random.randint(1, 3)\n", " banks = random.sample(bank_df['bank_name'].tolist(), num_accounts)\n", " for bank in banks:\n", " comp_bank_balance_data.append({\n", " 'tax_id': tax_id,\n", " 'bank_name': bank,\n", " 'balance': int(np.random.uniform(-5, 15) * 1000000000)\n", " })\n", "\n", "comp_bank_balance_df = pd.DataFrame(comp_bank_balance_data)" ] }, { "cell_type": "code", "execution_count": 7, "id": "6ac2a06c", "metadata": {}, "outputs": [], "source": [ "comp_bank_balance_df_total = dict(comp_bank_balance_df.groupby('tax_id')['balance'].sum())" ] }, { "cell_type": "code", "execution_count": 8, "id": "55713e2b", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pd.Series(comp_bank_balance_df_total.values()).hist()" ] }, { "cell_type": "code", "execution_count": 9, "id": "7240b9eb", "metadata": {}, "outputs": [], "source": [ "company_df['tong_du_no_tctd'] = company_df['tax_id'].apply(lambda x: comp_bank_balance_df_total[x])" ] }, { "cell_type": "code", "execution_count": 10, "id": "8e78d236", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Industry Table:\n", " industry_name\n", "0 medical supplies\n", "1 logistics\n", "2 construction\n", "\n", "Owner Table (first 5 rows):\n", " owner_cmnd owner_name owner_sdt \\\n", "0 030030002646 Đặng Thị Huy +84276460191 \n", "1 030069671486 Vũ Anh Anh +84940856172 \n", "2 030059740689 Phạm Kim Thanh +84679600097 \n", "3 030082937849 Vũ Tuấn Hạnh +84200558688 \n", "4 030063557540 Bùi Gia Cường +84232305928 \n", "\n", " owner_email \n", "0 angthihuy030030002646@gmail.com \n", "1 vuanhanh030069671486@gmail.com \n", "2 phamkimthanh030059740689@gmail.com \n", "3 vutuanhanh030082937849@gmail.com \n", "4 buigiacuong030063557540@gmail.com \n", "\n", "Employee Table (first 5 rows):\n", " empl_persnbr empl_name area title branch\n", "0 emp-22 Anthony Thái Nguyên title1 Thái Nguyên_3\n", "1 emp-54 Ryan Hà Tĩnh title1 Hà Tĩnh_1\n", "2 emp-4 Steven Hà Nội title2 Hà Nội_1\n", "3 emp-48 Brian Bình Phước title3 Bình Phước_1\n", "4 emp-36 David Nghệ An title1 Nghệ An_2\n", "\n", "Company Table (first 5 rows):\n", " tax_id owner_cmnd org_type nganh_trong_tam mnsegmtcd \\\n", "0 7622272427 030082937849 KHMOI construction SME \n", "1 4229821024 030065535693 KHMOI medical supplies SME \n", "2 6859830730 030091528629 KHMOI medical supplies SME \n", "3 4988018261 030084940335 KHMOI logistics MMLC \n", "4 8603639430 030068682103 KHMOI logistics SME \n", "\n", " post_tel ma_pers_nv addrname gpkdngaycap tpktdesc \\\n", "0 +842041765354 emp-11 Gia Lai 2019-05-24 Tư nhân \n", "1 +842063951610 emp-32 Bến Tre 2017-11-14 Tập thể \n", "2 +842062607791 emp-51 Đồng Tháp 2018-09-12 Có vốn đầu tư nước ngoài \n", "3 +842016101585 emp-7 Trà Vinh 2020-12-14 Tập thể \n", "4 +842038397182 emp-50 Đồng Tháp 2017-10-09 Có vốn đầu tư nước ngoài \n", "\n", " ... MAT_CAN_DOI_VON_2022 TY_SO_THANH_TOAN_2021 TY_SO_THANH_TOAN_2022 \\\n", "0 ... N 1.962874 1.962874 \n", "1 ... Y 0.654412 0.654412 \n", "2 ... Y 0.751315 0.751315 \n", "3 ... N 1.217314 1.217314 \n", "4 ... N 1.890772 1.890772 \n", "\n", " DOANH_THU_2021 DOANH_THU_2022 LOI_NHUAN_TRUOC_THUE_2021 \\\n", "0 48716552765 48716552765 3787704286 \n", "1 47666922877 47666922877 813696553 \n", "2 32662938491 32662938491 4300998766 \n", "3 3393558680 3393558680 901644371 \n", "4 10552179068 10552179068 3325278700 \n", "\n", " LOI_NHUAN_TRUOC_THUE_2022 LOI_NHUAN_SAU_THUE_2021 \\\n", "0 3787704286 3010311964 \n", "1 813696553 1614863544 \n", "2 4300998766 3901861432 \n", "3 901644371 3211635643 \n", "4 3325278700 1004218780 \n", "\n", " LOI_NHUAN_SAU_THUE_2022 tong_du_no_tctd \n", "0 3010311964 9347261603 \n", "1 1614863544 18182442849 \n", "2 3901861432 24736886066 \n", "3 3211635643 -1485338235 \n", "4 1004218780 3350659588 \n", "\n", "[5 rows x 48 columns]\n", "\n", "Ledger Table (first 5 rows):\n", " provider_tax_id customer_tax_id vat_number total_vat_amount\n", "0 2501121117 2329033582 9 2159000000\n", "1 8504901545 8240753978 8 3946000000\n", "2 6505005896 4964191377 5 1317000000\n", "3 6297587346 3995231071 2 1290000000\n", "4 2771976966 5358816410 10 2011000000\n", "\n", "Bank Table:\n", " bank_name\n", "0 Vietcombank\n", "1 VietinBank\n", "2 BIDV\n", "3 Agribank\n", "4 Sacombank\n", "5 ACB\n", "6 Techcombank\n", "7 MB Bank\n", "8 VPBank\n", "9 HDBank\n", "10 TPBank\n", "11 VIB\n", "12 Eximbank\n", "13 SHB\n", "14 LienVietPostBank\n", "15 SCB\n", "16 ABBank\n", "17 SeABank\n", "18 OCB\n", "19 Nam A Bank\n", "20 Maritime Bank\n", "21 PGBank\n", "22 BaoVietBank\n", "23 Saigonbank\n", "24 KienLongBank\n", "\n", "CompBankBalance Table (first 5 rows):\n", " tax_id bank_name balance\n", "0 7622272427 ABBank -2806436630\n", "1 7622272427 VIB 12153698233\n", "2 4229821024 Maritime Bank 11798814574\n", "3 4229821024 Techcombank 6383628275\n", "4 6859830730 LienVietPostBank 11730408676\n" ] } ], "source": [ "# Print sample rows from each table\n", "print(\"Industry Table:\")\n", "print(industry_df)\n", "\n", "print(\"\\nOwner Table (first 5 rows):\")\n", "print(owner_df.head())\n", "\n", "print(\"\\nEmployee Table (first 5 rows):\")\n", "print(employee_df.head())\n", "\n", "print(\"\\nCompany Table (first 5 rows):\")\n", "print(company_df.head())\n", "\n", "print(\"\\nLedger Table (first 5 rows):\")\n", "print(ledger_df.head())\n", "\n", "print(\"\\nBank Table:\")\n", "print(bank_df)\n", "\n", "print(\"\\nCompBankBalance Table (first 5 rows):\")\n", "print(comp_bank_balance_df.head())\n", "\n", "# Optionally, save to CSV files\n", "industry_df.to_csv(\"industry.csv\", index=False)\n", "owner_df.to_csv(\"owner.csv\", index=False)\n", "employee_df.to_csv(\"employee.csv\", index=False)\n", "company_df.to_csv(\"company.csv\", index=False)\n", "ledger_df.to_csv(\"ledger.csv\", index=False)\n", "bank_df.to_csv(\"bank.csv\", index=False)\n", "comp_bank_balance_df.to_csv(\"comp_bank_balance.csv\", index=False)" ] }, { "cell_type": "code", "execution_count": 11, "id": "9734ad2e", "metadata": {}, "outputs": [], "source": [ "unique_check = [owner_df['owner_cmnd'],\n", " owner_df['owner_sdt'],\n", " employee_df['empl_persnbr'],\n", " company_df['tax_id'],\n", " company_df['org_name'],\n", " comp_bank_balance_df[['tax_id', 'bank_name']],\n", " ledger_df[['provider_tax_id', 'customer_tax_id']]\n", " ]\n", "for f in unique_check:\n", " assert max(f.value_counts()) == 1\n" ] }, { "cell_type": "code", "execution_count": 12, "id": "fe9e335b", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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4344 rows × 2 columns

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" ], "text/plain": [ " provider_tax_id customer_tax_id\n", "0 2501121117 2329033582\n", "1 8504901545 8240753978\n", "2 6505005896 4964191377\n", "3 6297587346 3995231071\n", "4 2771976966 5358816410\n", "... ... ...\n", "4339 4130017505 4857436304\n", "4340 7841215783 9442616091\n", "4341 9074492548 4227725113\n", "4342 5784403092 8616879459\n", "4343 2193998000 3476614206\n", "\n", "[4344 rows x 2 columns]" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "f" ] }, { "cell_type": "code", "execution_count": 13, "id": "6701cf30", "metadata": {}, "outputs": [], "source": [ "\n", "assert not (set(comp_bank_balance_df['tax_id']) - set(company_df['tax_id']))\n", "assert not (set(company_df['owner_cmnd'].dropna()) - set(owner_df['owner_cmnd']))\n", "assert not (set(company_df['ma_pers_nv']) - set(employee_df['empl_persnbr']))\n", "assert not (set(company_df['nganh_trong_tam']) - set(industry_df['industry_name']))\n", "assert not (set(ledger_df['provider_tax_id']) - set(company_df['tax_id']))\n", "assert not (set(ledger_df['customer_tax_id']) - set(company_df['tax_id']))\n" ] }, { "cell_type": "code", "execution_count": 14, "id": "727b25d5", "metadata": {}, "outputs": [], "source": [ "N_NEWS = N_COMPANY * 3\n", "comp_news = pd.DataFrame(\n", "{\n", " \"tax_id\": np.random.choice(company_df['tax_id'], N_NEWS),\n", " \"news\":[fake.sentence(nb_words=5, variable_nb_words=False) for _ in range(N_NEWS)],\n", " \"news_cls\": np.random.choice([\"good\", \"bad\", \"neutral\"], N_NEWS),\n", " \"news_date\": [generate_random_date() for _ in range (N_NEWS)]\n", "})\n", "\n", "\n", "comp_news = comp_news.reset_index()\n", "comp_news['stt'] = comp_news['index']\n", "del(comp_news['index'])\n", "comp_news[['stt', 'tax_id', 'news', 'news_cls', 'news_date']].to_csv(\"comp_news.csv\", index=False)" ] }, { "cell_type": "code", "execution_count": 15, "id": "0c3bb74f", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "approach_status\n", "fresh 1193\n", "- 506\n", "scouted 394\n", "failure 198\n", "scheduled 109\n", "success 100\n", "Name: count, dtype: int64" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "company_df['approach_status'].value_counts()" ] }, { "cell_type": "markdown", "id": "328421ae", "metadata": {}, "source": [ "# Meeting management\n", "\n", "(`approach_status` >= scheduled)" ] }, { "cell_type": "markdown", "id": "e6507d72", "metadata": {}, "source": [ "Bảng kết quả tiếp cận\n", "\n", "```\n", "session_id,\n", "empl\n", "taxid\n", "date\n", "method\n", "product\n", "note\n", "status\n", "```\n" ] }, { "cell_type": "code", "execution_count": 16, "id": "3c8ddcd0", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "approach_status\n", "fresh 1193\n", "- 506\n", "scouted 394\n", "failure 198\n", "scheduled 109\n", "success 100\n", "Name: count, dtype: int64" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "company_df['approach_status'].value_counts()" ] }, { "cell_type": "code", "execution_count": 17, "id": "d53317b8", "metadata": {}, "outputs": [], "source": [ "import uuid\n", "\n", "def generate_meet_date():\n", " # Generate a random year between 2012 and 2020\n", " year = np.random.choice([2023, 2024])\n", " # Generate a random month between 1 and 12\n", " month = np.random.randint(1, 6)\n", " # Generate a random day between 1 and 28\n", " day = np.random.randint(1, 29)\n", " # Format the date as a string\n", " random_date = f\"{year:04d}-{month:02d}-{day:02d}\"\n", " return random_date\n", "\n", "\n", "meeting_df = company_df[company_df[\"approach_status\"].isin(['failure', 'success', 'scheduled'])][['tax_id', 'ma_pers_nv', 'approach_status']]\n", "meeting_df['session_id'] = [uuid.uuid1().hex for _ in range(len(meeting_df))]\n", "meeting_df['method'] = [random.choice(['gọi điện', 'hẹn gặp', 'gửi email']) for _ in range(len(meeting_df))]\n", "meeting_df['product'] = \"empty\"\n", "meeting_df['session_note'] = \"empty\"\n", "meeting_df['session_date'] = [generate_meet_date() for _ in range(len(meeting_df))]" ] }, { "cell_type": "code", "execution_count": 18, "id": "2b886a83", "metadata": {}, "outputs": [], "source": [ "meeting_df = meeting_df[['session_id', \"tax_id\", \"ma_pers_nv\", \"approach_status\", \"method\", \"product\", \"session_date\", \"session_note\"]]" ] }, { "cell_type": "code", "execution_count": 19, "id": "ea73e145", "metadata": {}, "outputs": [], "source": [ "meeting_df.to_csv(\"sales_session.csv\", index=False)" ] }, { "cell_type": "code", "execution_count": 20, "id": "ea235230", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "4 32\n", "14 32\n", "18 32\n", "30 32\n", "31 32\n", " ..\n", "2444 32\n", "2445 32\n", "2457 32\n", "2462 32\n", "2470 32\n", "Name: session_id, Length: 407, dtype: int64" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "meeting_df['session_id'].str.len()" ] }, { "cell_type": "code", "execution_count": 21, "id": "765d95aa", "metadata": {}, "outputs": [], "source": [ "def simulate_crawl_6161():\n", " return {\n", " \"Mã số thuế\": f\"{fake.unique.random_number(digits=10, fix_len=True)}\",\n", " \"Số điện thoại\": f\"+84{fake.unique.random_number(digits=9, fix_len=True)}\",\n", " \"Ngày thành lập\": generate_random_date(),\n", " \"Đại diện\": generate_vietnamese_name(),\n", " \"Ngành nghề\": np.random.choice(industry_df['industry_name']),\n", " \"website\": \"abc.xyz.com\"\n", " }\n", " " ] }, { "cell_type": "code", "execution_count": 22, "id": "e0bd4ddd", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'Mã số thuế': '4764977535',\n", " 'Số điện thoại': '+84438112037',\n", " 'Ngày thành lập': '2015-07-05',\n", " 'Đại diện': 'Huỳnh Phước Phúc',\n", " 'Ngành nghề': 'logistics',\n", " 'website': 'abc.xyz.com'}" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "simulate_crawl_6161()" ] } ], "metadata": { "kernelspec": { "display_name": "mlenv", "language": "python", "name": "mlenv" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.9" }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": true, "sideBar": true, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": true, "toc_window_display": false } }, "nbformat": 4, "nbformat_minor": 5 }