tablemaster 2.1.8

tablemaster is a Python toolkit for moving and managing tabular data across databases, Feishu/Lark, Google Sheets, and local files with one consistent API.

tablemaster

tablemaster is a Python toolkit for moving and managing tabular data across databases, Feishu/Lark, Google Sheets, and local files with one consistent API.

Why tablemaster

• Unified DataFrame-first API across multiple data backends
• Production-friendly DB helpers (query, execute, chunked upload, upsert)
• Built-in Feishu and Google Sheets connectors
• Local CSV/Excel ingestion utilities
• Declarative two-way sync between Feishu Sheet and database table
• Schema-as-Code workflow (init → schema plan → schema apply)
• Configuration-first design for reproducible automation

Installation

Install core package:

pip install -U tablemaster

Install backend-specific extras as needed:

pip install -U "tablemaster[database]" # Database support (MySQL/TiDB)
pip install -U "tablemaster[feishu]"   # Feishu/Lark connectors
pip install -U "tablemaster[gspread]"  # Google Sheets connectors
pip install -U "tablemaster[local]"    # Local CSV/Excel helpers
pip install -U "tablemaster[schema]"   # Schema management + SQLAlchemy + MySQL/PostgreSQL DB drivers
pip install -U "tablemaster[all]"      # Everything above

Configuration

Load configuration with:

import tablemaster as tm

cfg = tm.load_cfg()

Load config from another path:

import os
import tablemaster as tm

cfg = tm.load_cfg(path="C:/configs/tablemaster/prod.yaml")
cfg = tm.load_cfg(path="C:/configs/tablemaster")

os.environ["TM_CFG_PATH"] = "D:/ops/tablemaster/cfg.yaml"
cfg = tm.load_cfg()

load_cfg() resolves config file in this order:

1. Explicit path argument
2. TM_CFG_PATH environment variable
3. ./cfg.yaml
4. ~/.tablemaster/cfg.yaml

Example cfg.yaml:

mydb:
  host: 10.0.0.1
  user: admin
  password: secret
  database: bake_prod
  port: 3306
  db_type: mysql

db_tidb:
  host: sh.internal
  user: reader
  password: xxx
  database: analytics
  db_type: tidb
  use_ssl: true
  ssl_ca: /path/to/ca.pem

db_pg:
  host: 10.0.0.2
  user: postgres
  password: secret
  database: analytics
  port: 5432
  db_type: postgresql

feishu_prod:
  feishu_app_id: cli_xxx
  feishu_app_secret: yyy

gsheet:
  service_account_path: /absolute/path/to/service_account.json

For Google Sheets authentication setup, see: https://docs.gspread.org/en/latest/oauth2.html

Quick Start

Query and execute SQL

import tablemaster as tm

cfg = tm.load_cfg()
df = tm.query("SELECT * FROM orders LIMIT 20", cfg.mydb)
tm.opt("ALTER TABLE orders RENAME COLUMN old_col TO new_col", cfg.mydb)

Manage database tables

import tablemaster as tm

cfg = tm.load_cfg()
tb = tm.ManageTable("orders", cfg.mydb)
tb.upload_data(df, add_date=True)
tb.upsert_data(df, key="order_id")
tb.par_del("order_date > '2023-01-01'")

Google Sheets

import tablemaster as tm

cfg = tm.load_cfg()
sheet = ("spreadsheet_id_or_name", "worksheet_name")
df = tm.gs_read_df(sheet, cfg.gsheet)
tm.gs_write_df(sheet, df, cfg.gsheet)

Feishu / Lark

import tablemaster as tm

cfg = tm.load_cfg()
feishu_sheet = ("spreadsheet_token", "sheet_id")
feishu_base = ("app_token", "table_id")

sheet_df = tm.fs_read_df(feishu_sheet, cfg.feishu_prod)
base_df = tm.fs_read_base(feishu_base, cfg.feishu_prod)
tm.fs_write_df(feishu_sheet, sheet_df, cfg.feishu_prod, loc="A1", clear_sheet=False)
tm.fs_write_base(feishu_base, base_df, cfg.feishu_prod, clear_table=False)

Local files

import tablemaster as tm

single_df = tm.read("*orders_2026*")
merged_df = tm.batch_read("*orders_2026*")
df_list = tm.read_dfs("*orders_2026*")

Declarative two-way sync

import tablemaster as tm

cfg = tm.load_cfg()
feishu_sheet = ("spreadsheet_token", "sheet_id")

merged = tm.sync(
    source=("feishu", feishu_sheet, cfg.feishu_prod),
    target=("db", cfg.mydb, "orders"),
    on_conflict="upsert",
    key="order_id",
)

Schema management (YAML-driven)

tablemaster init
tablemaster schema plan mydb --cfg-path ./cfg.yaml
tablemaster schema apply mydb --cfg-path ./cfg.yaml
tablemaster schema pull mydb --cfg-path ./cfg.yaml

Example schema file:

table: ods_orders
columns:
  - name: id
    type: BIGINT
    primary_key: true
    nullable: false
  - name: order_no
    type: VARCHAR(64)
    nullable: false
indexes:
  - name: idx_order_no
    columns: [order_no]
    unique: true

CLI

tablemaster now ships with a built-in CLI:

tablemaster --help
python -m tablemaster --help

Commands:

tablemaster version-info
tablemaster config list --cfg-path ./cfg.yaml
tablemaster config show mydb --cfg-path ./cfg.yaml
tablemaster db query "SELECT * FROM orders LIMIT 20" --cfg-key mydb --cfg-path ./cfg.yaml
tablemaster db query "SELECT * FROM orders" --cfg-key mydb --output ./out/orders.csv
tablemaster local read "*orders_2026*" --limit 10
tablemaster local read "*orders_2026*" --no-det-header
tablemaster init --cfg-path ./cfg.yaml
tablemaster schema plan mydb --cfg-path ./cfg.yaml --output ./plan.json
tablemaster schema apply mydb --cfg-path ./cfg.yaml --plan-file ./plan.json --auto-approve
tablemaster schema pull mydb --cfg-path ./cfg.yaml --output-dir ./schema

CLI command groups:

• version-info: Print installed package version.
• 
  
• config show <cfg_key>: Print one config entry as JSON.
• db query <sql>: Run SQL with --cfg-key; use --limit to control stdout preview and --output to export full result as CSV.
• local read <pattern>: Read one local CSV/Excel match and print preview; use --det-header/--no-det-header to control header detection.
• config list: List top-level keys from config.
• init: Bootstrap cfg.yaml and schema/<connection>/ scaffold in current directory.
• schema plan <connection>: Compare YAML schema and live DB, print/apply-safe plan.
• schema apply <connection>: Execute DDL actions from generated or saved plan.
• schema pull <connection>: Generate YAML schema files from live DB tables.

--cfg-path accepts either a config file path or a directory containing cfg.yaml.

Public API

• Database: query, opt, ManageTable
• Feishu/Lark: fs_read_df, fs_write_df, fs_read_base, fs_write_base
• Google Sheets: gs_read_df, gs_write_df
• Local files: read, batch_read, read_dfs
• Sync: sync
• Config: load_cfg
• Schema: load_schema_definitions, introspect_tables, generate_plan, render_plan, save_plan, load_plan, apply_plan, init_scaffold, pull_schema, write_pulled_schema

Notes

• Python 3.9+ is required.
• CLI entrypoint is tablemaster; use tablemaster --help for command details.
• tm.cfg and read_cfg() are backward-compatible but deprecated in favor of load_cfg().
• tablemaster[database] is the recommended DB extra name; tablemaster[mysql] remains available for compatibility.
• PostgreSQL is supported (db_type: postgresql) for query/execute and upsert (ON CONFLICT).
• For PostgreSQL dependencies, use tablemaster[schema] or install SQLAlchemy + psycopg2-binary manually.