Function andrews_curves

pandas/plotting/_matplotlib/misc.py:223–287

(
    frame: DataFrame,
    class_column,
    ax: Axes | None = None,
    samples: int = 200,
    color=None,
    colormap=None,
    **kwds,
)

def andrews_curves(
    frame: DataFrame,
    class_column,
    ax: Axes | None = None,
    samples: int = 200,
    color=None,
    colormap=None,
    **kwds,
) -> Axes:
    import matplotlib.pyplot as plt

    def function(amplitudes):
        def f(t):
            x1 = amplitudes[0]
            result = x1 / np.sqrt(2.0)

            # Take the rest of the coefficients and resize them
            # appropriately. Take a copy of amplitudes as otherwise numpy
            # deletes the element from amplitudes itself.
            coeffs = np.delete(np.copy(amplitudes), 0)
            coeffs = np.resize(coeffs, (int((coeffs.size + 1) / 2), 2))

            # Generate the harmonics and arguments for the sin and cos
            # functions.
            harmonics = np.arange(0, coeffs.shape[0]) + 1
            trig_args = np.outer(harmonics, t)

            result += np.sum(
                coeffs[:, 0, np.newaxis] * np.sin(trig_args)
                + coeffs[:, 1, np.newaxis] * np.cos(trig_args),
                axis=0,
            )
            return result

        return f

    n = len(frame)
    class_col = frame[class_column]
    classes = frame[class_column].drop_duplicates()
    df = frame.drop(class_column, axis=1)
    t = np.linspace(-np.pi, np.pi, samples)
    used_legends: set[str] = set()

    color_values = get_standard_colors(
        num_colors=len(classes), colormap=colormap, color_type="random", color=color
    )
    colors = dict(zip(classes, color_values, strict=False))
    if ax is None:
        ax = plt.gca()
        ax.set_xlim(-np.pi, np.pi)
    for i in range(n):
        row = df.iloc[i].values
        f = function(row)
        y = f(t)
        kls = class_col.iat[i]
        label = pprint_thing(kls)
        if label not in used_legends:
            used_legends.add(label)