DataFrame.plot.hexbin(self, x, y, C=None, reduce_C_function=None, gridsize=None, **kwargs)
[source]
Generate a hexagonal binning plot.
Generate a hexagonal binning plot of x
versus y
. If C
is None
(the default), this is a histogram of the number of occurrences of the observations at (x[i], y[i])
.
If C
is specified, specifies values at given coordinates (x[i], y[i])
. These values are accumulated for each hexagonal bin and then reduced according to reduce_C_function
, having as default the NumPy’s mean function (numpy.mean()
). (If C
is specified, it must also be a 1-D sequence of the same length as x
and y
, or a column label.)
Parameters: |
|
---|---|
Returns: |
|
See also
DataFrame.plot
matplotlib.pyplot.hexbin
The following examples are generated with random data from a normal distribution.
>>> n = 10000 >>> df = pd.DataFrame({'x': np.random.randn(n), ... 'y': np.random.randn(n)}) >>> ax = df.plot.hexbin(x='x', y='y', gridsize=20)
The next example uses C
and np.sum
as reduce_C_function
. Note that ‘observations’
values ranges from 1 to 5 but the result plot shows values up to more than 25. This is because of the reduce_C_function
.
>>> n = 500 >>> df = pd.DataFrame({ ... 'coord_x': np.random.uniform(-3, 3, size=n), ... 'coord_y': np.random.uniform(30, 50, size=n), ... 'observations': np.random.randint(1,5, size=n) ... }) >>> ax = df.plot.hexbin(x='coord_x', ... y='coord_y', ... C='observations', ... reduce_C_function=np.sum, ... gridsize=10, ... cmap="viridis")
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https://pandas.pydata.org/pandas-docs/version/0.25.0/reference/api/pandas.DataFrame.plot.hexbin.html