#Anaconda contextlib2 python 3 install how to
#Anaconda contextlib2 python 3 install install

The collection demands more steps to reproduce and takes much longer to run (months).

#Anaconda contextlib2 python 3 install install

Go to the analyses folder and install all the dependencies of the requirements.txt cd jupyter_reproducibility/analysesДЇor reproducing the analyses, run jupyter on this folder: jupyter notebook Now, configure the connection string for sqlalchemy by setting the environment variable JUP_DB_CONNECTTION: export and extract jupyter_2: tar -xjf jupyter_2Д¬reate a conda environment with Python 3.7: conda create -n analyses python=3.7 Create a database in PostgreSQL (we call it 'jupyter'), and use psql to restore the dump: psql jupyter < db.dump In the analysis, we used the following environment:ДЇirst, download 2 and extract it: tar -xjf 2

#Anaconda contextlib2 python 3 install how to

This section shows how to load the data in the database and run the analyses notebooks.

In the remaining of this text, we give instructions for reproducing the analyses, by using the data provided in the dump and reproducing the collection, by collecting data from GitHub again. The notebook analyses/N12.To.Paper.ipynb moves data to it archaeology: this folder has all the scripts we use to query, download, and extract data from GitHub notebooks.analyses: this folder has all the notebooks we use to analyze the data in the PostgreSQL database.

The jupyter_2 file contains all the scripts we used to query and download Jupyter Notebooks, extract data from them, and analyze the data. The 2 file contains a PostgreSQL dump of the database, with all the data we extracted from the notebooks. To understand good and bad practices used in the development of real notebooks, we analyzed 1.4 million notebooks from GitHub. At the same time, there has been growing criticism that the way notebooks are being used leads to unexpected behavior, encourage poor coding practices and that their results can be hard to reproduce. The self-documenting aspects and the ability to reproduce results have been touted as significant benefits of Jupyter Notebooks.