Installing and getting started¶
Official releases of seaborn can be installed from PyPI:
pip install seaborn
The library is also included as part of the Anaconda distribution:
conda install seaborn
Supported Python versions¶
If not already present, these libraries will be downloaded when you install seaborn.
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")
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 seaborn as sns import matplotlib.pyplot as plt
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
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
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
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
python -m pip install seaborn.
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
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 or discourse, which have a larger audience of people who will see your post and may be able to offer assistance. StackOverflow is better for specific issues, while discourse is better for more open-ended discussion. 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.