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Installing (compiler) directly from GitHub

pip install git+

See this SO post for further install options.

Google Cloud Datalab

  • Installation: pip install quilt --user, the User icon (upper right) > About > Restart server
  • Prefer Python 3 kernels

Jupyter: quilt login doesn't present textbox for user login code

  • Try a Python 3 kernel

ImportError on import of data package

Ensure that that the package has been installed via quilt install.

(Windows) ImportError when accessing package contents

pyarrow module, used by quilt, may fail to import because of missing DLLs:

  File "C:\Program Files\Python36\lib\site-packages\pyarrow\", line 32, in <module>
    from pyarrow.lib import cpu_count, set_cpu_count
ImportError: DLL load failed: The specified module could not be found.

Make sure you have installed Visual C++ Redistributable for Visual Studio 2015.

Jupyter, virtual environments, quilt not found

When working with virtual environments like conda create, jupyter can be installed in the root environment. If you then install and run quilt in another environment, foo, Jupyter will not be able to find quilt.


Install quilt in the root environment, or install Jupyter in foo (run which jupyter in Jupyter's Terminal to ensure that you're using the environment local Jupyter).

Alternatively, pip install quilt from Jupyter's Terminal.

pandas index_col

This keyword argument should be temporarily avoided in build.yml as it causes pyarrow to hiccup on serialization.

Packages missing after upgrade to Quilt 2.8

Quilt 2.8 changes where data packages are stored on your local machine. As a result, Quilt will no longer look for packages in quilt_packages directories. You will need to reinstall any previously installed packages. Locally built packages can be rebuilt. Or, to migrate existing packages to the new store without rebuilding, first revert to an ealier version of Quilt, then push your packages to the Quilt registry.

pip install quilt==2.7.1
quilt push <your_username>/<your_package>

Once your packages are stored at the registry, you can upgrade to quilt 2.8.0 (or later) and re-install them.

pip install --upgrade quilt
quilt install <your_username>/<your_package>

Exception when installing quilt on OS X El Capitan

pip may try to upgrade pyOpenSSL, and fail with the following exception when removing the old version of the package:

OSError: [Errno 1] Operation not permitted: '/tmp/pip-zFP4QS-uninstall/System/Library/Frameworks/Python.framework/Versions/2.7/Extras/lib/python/pyOpenSSL-0.13.1-py2.7.egg-info'

This problem is not specific to quilt, and is caused by outdated packages in OS X. See this stackoverflow question for more information.


  • Use a virtual environment such as conda or virtualenvwrapper
  • Upgrade pyOpenSSL using brew or easy_install
  • Upgrade to a more recent version of OS X

ArrowNotImplementedError when saving a large dataframe

Unfortunately, this is caused by a bug in pyarrow.

There does not appear to be a way to save a dataframe with a string column whose size is over 2GB. It is possible, however, to split it up into multiple dataframes - which will then get merged into one when accessed.


Suppose the problematic dataframe is called big_data, it comes from big_data.csv, and the root of your package is in my_dir.

First, delete the dataframe from the build file, my_dir/build.yml. (If you were building directly from a directory, then run quilt generate my_dir first.)

Build a temporary package that contains the rest of the data:

quilt build user/pkg_partial my_dir/build.yml

Open a Python shell or write a script, and manually build the final package:

import quilt
import pandas as pd
from import pkg_partial

# Read the dataframe.
data = pd.read_csv('my_dir/big_data.csv')

# Add it to the partial package.
# You will need to adjust the number of pieces and number of rows per piece
pkg_partial._set(['big_data', 'part1'], data[0:1500000])
pkg_partial._set(['big_data', 'part2'], data[1500000:])

# Build the final package.'user/pkg', pkg_partial)

# Import the new package.
from import pkg

# Get a merged dataframe. You can also access pkg.big_data.part1(), etc. if needed.
new_data = pkg.big_data()

# Make sure the dataframe in the package is in fact the same as the original.
assert new_data.equals(data)

Symlinking with Windows has a few OS quirks to be mindful of. If you're encountering difficulty getting symlinking to work, you might try the following:

  • Ensure Windows is fully updated (known related bugs exist)
  • Escalate administrator privileges ("run as admin"), or validate user privileges
    • See this SuperUser article for relevant instructions
    • If UAC is on
      • If user is not an administrator, they must have the Create Symbolic Links privilege
      • If user is an administrator, they must escalate privileges, even if they have the Create Symbolic Links privilege
        • This means if you want a user to create symlinks without requiring escalation, they may not be an administrator.
    • If UAC is off
      • Any user with the Create Symbolic Links privilege may do so
  • Folder-level privileges may interfere with symlinking
    • Verify there are no folder-specific restrictions on privileges
  • Symlink type may be disabled, as it is by default for remote->remote symlinks
    • Use fsutil (from an elevated command prompt) to evaluate and/or enable acceptable symlink types
      • fsutil: For advanced users only. See the Microsoft documentation on fsutil)
        • fsutil behavior query SymlinkEvaluation will display the current state of symlink evaluation
        • Use fsutil behavior set SymlinkEvaluation R2R:1 to enable (for example) remote-to-remote symlinks

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