Skip to main content

A quick and easy way to convert a Pandas DataFrame to a Tableau extract.

Project description


A quick and easy way to convert a Pandas DataFrame to a Tableau .tde or .hyper extract.

Getting Started


  • If you want to output as a .tde format, you'll need to install TableauSDK directly from Tableau's site here.
  • If you want to output as a .hyper format, you'll need to install Extract API 2.0 directly from Tableau's site here.
  • Although Tableau's site claims Python 3 is not supported, this module is tested to work fully functional on Python 3.6.


Once installing TableauSDK is done, download this repository, navigate to your downloads file and run the following in cmd or terminal:

python -m install

You can also install pandleau using pip:

pip install pandleau

But note that this will throw a warning to install tableausdk using the above link in Prerequisites.


I grabbed the following Brazil flights data off of kaggle for this example:

import pandas as pd
from pandleau import *

# Import the data
example_df = pd.read_csv(r'example/BrFlights2.csv', encoding = 'iso-8859-1')

# Format dates in pandas
example_df['Partida.Prevista'] = pd.to_datetime(example_df['Partida.Prevista'], format = '%Y-%m-%d')
example_df['Partida.Real'] = pd.to_datetime(example_df['Partida.Real'], format = '%Y-%m-%d')
example_df['Chegada.Prevista'] = pd.to_datetime(example_df['Chegada.Prevista'], format = '%Y-%m-%d')
example_df['Chegada.Real'] = pd.to_datetime(example_df['Chegada.Real'], format = '%Y-%m-%d')

# Set up a spatial column
example_df.loc[:, 'SpatialDest'] = example_df['LongDest'].apply( lambda x: "POINT (" + str( round(x, 6) ) ) + \
	example_df['LatDest'].apply( lambda x: " "+str( round(x, 6) ) + ")" )

# Change to pandleau object
df_tableau = pandleau(example_df)

# Define spatial column
df_tableau.set_spatial('SpatialDest', indicator=True)

# Write .tde or .hyper Extract!
df_tableau.to_tableau('test.hyper', add_index=False)

Tableau Server/Online Automation

Eric Chan (erickhchan) wrote a really cool blog post on using Python to blend and clean data before pushing it to Tableau Online (which is a SaaS version of Tableau Server). This is a great way to learn how to automate the data refresh process with Tableau Server Client and Pandleau. Check out his blog post here:


Related Project

RTableau Convert R data.frame to Tableau Extract using pandleau


This project is licensed under the MIT License - see the file for details

Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

pandleau-0.4.1.tar.gz (5.1 kB view hashes)

Uploaded source

Built Distribution

pandleau-0.4.1-py3-none-any.whl (6.2 kB view hashes)

Uploaded py3

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page