NOTICE: pandas-td is no longer maintained and planned to be archived on GitHub on April 17, 2020. Use pytd instead.
See a guide to replacing pandas-td with pytd and its official documentation for more information.
Pandas-TD [deprecated]
Documentation
- Tutorial (https://github.com/treasure-data/pandas-td/blob/master/doc/tutorial.ipynb)
- Magic functions (https://github.com/treasure-data/pandas-td/blob/master/doc/magic.ipynb)
Install
You can install the releases from PyPI:
$ pip install pandas-tdOn Mac OS X, you can install Pandas and Jupyter as follows:
# Use Homebrew to install Python 3.x
$ brew install python3
# Install pandas, pandas-td, and jupyter
$ pip3 install pandas pandas-td jupyter
# Set API key and start a session
$ export TD_API_KEY=...
$ jupyter notebookExamples
import pandas_td as td
# Initialize query engine
engine = td.create_engine('presto:sample_datasets')
# Read Treasure Data query into a DataFrame.
df = td.read_td('select * from www_access', engine)
# Read Treasure Data table into a DataFrame.
df = td.read_td_table('nasdaq', engine, limit=10000)
# Write a DataFrame to a Treasure Data table.
con = td.connect()
td.to_td(df, 'my_db.test_table', con, if_exists='replace', index=False)Magic functions (experimental):
In [1]: %%load_ext pandas_td.ipython
# Use database
In [2]: %td_use sample_datasets
# Run query
In [3]: %%td_presto
...: select * from www_accessLicense
Apache Software License, Version 2.0
