Tutorial Setup Instructions

Please come to SciPy with your computer set up for your tutorials. If you have trouble, tutorial instructors will be available to assist the morning of the tutorial from 8:30 am to 9:00 am. You may also email questions to SciPy@enthought.com. 

If you registered for beginner tutorials, please set your computers up for Tensorflow and Introduction to Visualization.

If you registered for advanced tutorials, please set your computer up for Advanced NumPy and Advanced Machine Learning.

Please bring your computer fully charged. We will have charging stations around the ballroom and foyer, but we will not have power at individual seats.

We set “SCIPY” as a WIFI access code. Please select SSID “MandarinOriental” and go to “I HAVE AN ACESS CODE”.  

April 23


We will use Colab (https://colab.research.google.com) for our tutorial, a web-based Jupyter environment that includes a GPU. Attendees are welcome to install TensorFlow locally if they prefer, and use Jupyter or their favorite text editor. 

Advanced NumPy

Please install 




as well as Jupyter notebooks.

April 24

Introduction to Visualization

# 事前準備 (Attendees preparation instructions)


- ノートパソコン (Windows / macOS / Linux)

  - ChromeまたはFirefoxが動作する

  - Python 3.6 または Python 3.7 が動作する

- 環境準備

  - Python 3.6 または Python 3.7のインストール

  - Jupyter Notebookのインストール

  - Matplotlib、pandas、NumPyのインストール


なお、環境準備に不安のある方は、チュートリアル中に利用できるColaboratory(Google)を使って受講が出来るようにしますので、googleアカウント(gmailアカウントなど)を準備し、https://colab.research.google.com にて利用の開始を行ってください。

Advanced Machine Learning

For this tutorial the requirements are the following:


"beautifulsoup4""html5lib" "jupyter" "lxml" "matplotlib" "nltk" "numpy" "openpyxl" "pandas>=0.23.0" "pandas<0.24.0" "pandas-datareader" "pip" "pyqt" "pytables" "requests" "scikit-learn>=0.20.0" "scikit-learn<0.21.0" "scikits.image>0.14.0" "scipy" "seaborn" "setuptools" "spacy" "spacy-en-core-web-sm" "sqlalchemy" "statsmodels" "xlrd"


If you're using Enthought EDM [1], you can download the bundle for you platform below, and import it as the "ml-tutorial" environment with:


$ edm envs import ml-tutorial -f PATH_TO_BUNDLE






[1]: https://www.enthought.com/product/enthought-deployment-manager/#download-edm


If you're using conda, you can create the "ml-tutorial" environment with:


conda create -n ml-tutorial python=3 "beautifulsoup4" "html5lib" "jupyter" "lxml" "matplotlib" "nltk" "numpy" "openpyxl" "pandas>=0.23.0" "pandas<0.24.0" "pandas-datareader" "pip" "pyqt" "pytables" "requests" "scikit-learn>=0.20.0" "scikit-learn<0.21.0" "scikits.image>0.14.0" "scipy" "seaborn" "setuptools" "spacy" "spacy-en-core-web-sm" "sqlalchemy" "statsmodels" "xlrd"




If you have any additional questions, swing by the SciPy Japan registration desk or email scipy@enthought.com.