pytableau 0.9.0

The unified Python SDK for Tableau workbook engineering

pytableau

What pandas did for tabular data, pytableau does for Tableau workbooks.

pytableau is the unified Python SDK for Tableau workbook engineering. It brings every layer of Tableau workbook manipulation — data, connections, semantic model, presentation, packaging, and server lifecycle — under one coherent, Pythonic API.

Why pytableau?

The Tableau Python ecosystem is fragmented. Tableau/Salesforce maintains several narrow libraries, each covering a single concern:

Library	Layer	Limitation
tableaudocumentapi	Connection XML	No calc fields, no viz edits, abandoned 2021
tableauhyperapi	.hyper data files	No XML awareness
pantab	DataFrame ↔ .hyper bridge	No XML awareness
tableauserverclient	Server REST API	No local file manipulation

pytableau wraps and integrates all of them behind a single API.

Install

# Core (XML + ZIP only — no optional dependencies)
pip install pytableau

# With Hyper / extract support
pip install "pytableau[hyper]"

# With pandas convenience methods
pip install "pytableau[pandas]"

# With Tableau Server/Cloud integration
pip install "pytableau[server]"

# Everything
pip install "pytableau[all]"

Quick Start

Open and inspect a workbook

from pytableau import Workbook

wb = Workbook.open("sales_dashboard.twbx")

print(wb.version)                    # "2024.1"
print(wb.datasources.names)          # ["Sales Data", "Parameters"]
print(wb.worksheets.names)           # ["Revenue by Region", "Trend"]
print(wb.dashboards.names)           # ["Executive Summary"]

catalog = wb.catalog()
for field in catalog.calculated_fields:
    print(f"{field.caption}: {field.formula}")

Swap database connections (CI/CD promotion)

from pytableau import Workbook

wb = Workbook.open("report.twbx")
for ds in wb.datasources:
    for conn in ds.connections:
        conn.server = "prod-db.corp.com"
        conn.dbname = "analytics_prod"
wb.save_as("report_prod.twbx")

Inject a DataFrame

from pytableau import Workbook
import pandas as pd

wb = Workbook.open("template.twbx")
df = pd.read_csv("fresh_data.csv")
wb.datasources["Sales Data"].hyper.from_dataframe(df)
wb.save_as("refreshed_report.twbx")

Generate a workbook from a template

from pytableau import Workbook
import pandas as pd

df = pd.read_csv("q4_revenue.csv")

wb = Workbook.from_template("bar_chart")
wb.datasources["__PLACEHOLDER_DS__"].hyper.from_dataframe(df)
wb.template.map_fields({
    "__DIMENSION__": "Product Category",
    "__MEASURE__":   "Revenue",
    "__COLOR__":     "Region",
})
wb.parameters["__PARAM_TITLE__"].value = "Q4 2025 Revenue"
wb.save_as("q4_report.twbx")

CLI

pytableau inspect workbook.twbx
pytableau diff before.twb after.twb
pytableau swap workbook.twb --server prod-db.corp.com --db analytics_prod

Development Status

Phase	Description	Status
0	Foundation: scaffolding, packaging, constants	In Progress
1	Read & Inspect: parse any .twb/.twbx	Planned
2	Connection & Field Mutation	Planned
3	Data Layer: .hyper integration	Planned
4	Template Engine	Planned
5	Server Integration	Planned
6	Advanced Features	Planned

Design Principles

1. Layer cake, not monolith. Each Tableau layer maps to a distinct submodule. Engage at any abstraction level.
2. Batteries included, escape hatches available. The Pythonic API covers 90% of use cases; raw lxml nodes are always accessible.
3. Template-first for presentation. Build a viz in Desktop, then parameterize it with pytableau.
4. Fail loud, fail early. Validate XML mutations before writing — a corrupt .twb is the worst outcome.
5. Dependency-light by default. Core requires only lxml. Hyper, server, and pandas are optional extras.
6. Version-aware. Track Tableau Desktop version; warn on incompatible XML.

License

MIT — see LICENSE