databricks.koalas.DataFrame.plot.scatter

plot.scatter(x, y, s=None, c=None, **kwds)

Create a scatter plot with varying marker point size and color.

The coordinates of each point are defined by two dataframe columns and filled circles are used to represent each point. This kind of plot is useful to see complex correlations between two variables. Points could be for instance natural 2D coordinates like longitude and latitude in a map or, in general, any pair of metrics that can be plotted against each other.

Parameters

xint or str

The column name or column position to be used as horizontal coordinates for each point.

yint or str

The column name or column position to be used as vertical coordinates for each point.

sscalar or array_like, optional

cstr, int or array_like, optional

**kwds: Optional

Keyword arguments to pass on to databricks.koalas.DataFrame.plot().

Returns

:class:`matplotlib.axes.Axes` or numpy.ndarray of them

See also

matplotlib.pyplot.scatter

Scatter plot using multiple input data formats.

Examples

Let’s see how to draw a scatter plot using coordinates from the values in a DataFrame’s columns.

>>> df = ks.DataFrame([[5.1, 3.5, 0], [4.9, 3.0, 0], [7.0, 3.2, 1],
...                    [6.4, 3.2, 1], [5.9, 3.0, 2]],
...                   columns=['length', 'width', 'species'])
>>> ax1 = df.plot.scatter(x='length',
...                       y='width',
...                       c='DarkBlue')

And now with the color determined by a column as well.

>>> ax2 = df.plot.scatter(x='length',
...                       y='width',
...                       c='species',
...                       colormap='viridis')

databricks.koalas.DataFrame.plot.pie databricks.koalas.DataFrame.plot.density