Installing and getting started#

Official releases of seaborn can be installed from PyPI:

pip install seaborn

The basic invocation of pip will install seaborn and, if necessary, its mandatory dependencies. It is possible to include optional dependencies that give access to a few advanced features:

pip install seaborn[stats]

The library is also included as part of the Anaconda distribution, and it can be installed with conda:

conda install seaborn

As the main Anaconda repository can be slow to add new releaes, you may prefer using the conda-forge channel:

conda install seaborn -c conda-forge

Dependencies#

Supported Python versions#

  • Python 3.7+

Mandatory dependencies#

Optional dependencies#

  • statsmodels, for advanced regression plots

  • scipy, for clustering matrices and some advanced options

  • fastcluster, faster clustering of large matrices

Quickstart#

Once you have seaborn installed, you’re ready to get started. To test it out, you could load and plot one of the example datasets:

import seaborn as sns
df = sns.load_dataset("penguins")
sns.pairplot(df, hue="species")

If you’re working in a Jupyter notebook or an IPython terminal with matplotlib mode enabled, you should immediately see the plot. Otherwise, you may need to explicitly call matplotlib.pyplot.show():

import matplotlib.pyplot as plt
plt.show()

While you can get pretty far with only seaborn imported, having access to matplotlib functions is often useful. The tutorials and API documentation typically assume the following imports:

import numpy as np
import pandas as pd

import matplotlib as mpl
import matplotlib.pyplot as plt

import seaborn as sns
import seaborn.objects as so

Debugging install issues#

The seaborn codebase is pure Python, and the library should generally install without issue. Occasionally, difficulties will arise because the dependencies include compiled code and link to system libraries. These difficulties typically manifest as errors on import with messages such as "DLL load failed". To debug such problems, read through the exception trace to figure out which specific library failed to import, and then consult the installation docs for that package to see if they have tips for your particular system.

In some cases, an installation of seaborn will appear to succeed, but trying to import it will raise an error with the message "No module named seaborn". This usually means that you have multiple Python installations on your system and that your pip or conda points towards a different installation than where your interpreter lives. Resolving this issue will involve sorting out the paths on your system, but it can sometimes be avoided by invoking pip with python -m pip install seaborn.

Getting help#

If you think you’ve encountered a bug in seaborn, please report it on the GitHub issue tracker. To be useful, bug reports must include the following information:

  • A reproducible code example that demonstrates the problem

  • The output that you are seeing (an image of a plot, or the error message)

  • A clear explanation of why you think something is wrong

  • The specific versions of seaborn and matplotlib that you are working with

Bug reports are easiest to address if they can be demonstrated using one of the example datasets from the seaborn docs (i.e. with load_dataset()). Otherwise, it is preferable that your example generate synthetic data to reproduce the problem. If you can only demonstrate the issue with your actual dataset, you will need to share it, ideally as a csv.

If you’ve encountered an error, searching the specific text of the message before opening a new issue can often help you solve the problem quickly and avoid making a duplicate report.

Because matplotlib handles the actual rendering, errors or incorrect outputs may be due to a problem in matplotlib rather than one in seaborn. It can save time if you try to reproduce the issue in an example that uses only matplotlib, so that you can report it in the right place. But it is alright to skip this step if it’s not obvious how to do it.

General support questions are more at home on either stackoverflow, where there is a larger audience of people who will see your post and may be able to offer assistance. Your chance of getting a quick answer will be higher if you include runnable code, a precise statement of what you are hoping to achieve, and a clear explanation of the problems that you have encountered.