CODEWITHSUNDEEP
pythonmatplotlibpandasseaborn
FooBar
FooBar

Reputation: 16488

Plot joint distribution

I'm having a pandas dataset that contains an integer and a float value:

>>> df2[['AGE_REF', 'RETSURV']].dtypes
AGE_REF      int64
RETSURV    float64
dtype: object

I'd like to plot the joint distribution using pandas. I didn't see a simple way of pandas visualizing the joint distribution, but I stumbled across seaborn. So I tried to adjust code that I already found for my purposes:

>>> import seaborn as sns
>>> sns.jointplot('AGE_REF', "RETSURV", df2,
              kind="hex")
Traceback (most recent call last):
  File "<input>", line 2, in <module>
  File "/usr/local/lib/python2.7/site-packages/seaborn/distributions.py", line 969, in jointplot
    gridsize = int(np.mean([x_bins, y_bins]))
OverflowError: cannot convert float infinity to integer

I found a related bug report, so I tried to follow the workaround there - without success:

>>> sns.jointplot('AGE_REF', "RETSURV", df2,
              kind="hex", marginal_kws={"bins": 10})
Traceback (most recent call last):
  File "<input>", line 2, in <module>
  File "/usr/local/lib/python2.7/site-packages/seaborn/distributions.py", line 969, in jointplot
    gridsize = int(np.mean([x_bins, y_bins]))
OverflowError: cannot convert float infinity to integer

Upvotes: 0

Views: 1228

Answers (1)

mwaskom
mwaskom

Reputation: 49002

The default hexbin gridsize uses the same reference rule calculation as the histograms, so you'll need to set that directly too if you have data that violate those assumptions somehow:

sns.jointplot(x, y, kind="hex",
              joint_kws={"gridsize": 10},
              marginal_kws={"bins": 10})

Upvotes: 1

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