pandas.plotting.scatter_matrix

pandas.plotting.scatter_matrix(frame, alpha=0.5, figsize=None, ax=None, grid=False, diagonal='hist', marker='.', density_kwds=None, hist_kwds=None, range_padding=0.05, **kwargs)[source]

Draw a matrix of scatter plots.

Parameters:

frame:DataFrame

alpha:float, optional

Amount of transparency applied.

figsize:(float,float), optional

A tuple (width, height) in inches.

ax:Matplotlib axis object, optional

grid:bool, optional

Setting this to True will show the grid.

diagonal:{‘hist’, ‘kde’}

Pick between ‘kde’ and ‘hist’ for either Kernel Density Estimation or Histogram plot in the diagonal.

marker:str, optional

Matplotlib marker type, default ‘.’.

density_kwds:keywords

Keyword arguments to be passed to kernel density estimate plot.

hist_kwds:keywords

Keyword arguments to be passed to hist function.

range_padding:float, default 0.05

Relative extension of axis range in x and y with respect to (x_max - x_min) or (y_max - y_min).

**kwargs

Keyword arguments to be passed to scatter function.

Returns:

numpy.ndarray

A matrix of scatter plots.

Examples

>>> df = pd.DataFrame(np.random.randn(1000, 4), columns=['A','B','C','D'])
>>> pd.plotting.scatter_matrix(df, alpha=0.2)
array([[<Axes: xlabel='A', ylabel='A'>, <Axes: xlabel='B', ylabel='A'>,
        <Axes: xlabel='C', ylabel='A'>, <Axes: xlabel='D', ylabel='A'>],
       [<Axes: xlabel='A', ylabel='B'>, <Axes: xlabel='B', ylabel='B'>,
        <Axes: xlabel='C', ylabel='B'>, <Axes: xlabel='D', ylabel='B'>],
       [<Axes: xlabel='A', ylabel='C'>, <Axes: xlabel='B', ylabel='C'>,
        <Axes: xlabel='C', ylabel='C'>, <Axes: xlabel='D', ylabel='C'>],
       [<Axes: xlabel='A', ylabel='D'>, <Axes: xlabel='B', ylabel='D'>,
        <Axes: xlabel='C', ylabel='D'>, <Axes: xlabel='D', ylabel='D'>]],
      dtype=object)