code: def andrews_curves(
      frame: DataFrame,
      class_column: str,
      ax: Axes | None = None,
      samples: int = 200,
      color: list[str] | tuple[str, ...] | None = None,
      colormap=None,
      **kwargs,
  ) -> Axes:
      """
      Generate a matplotlib plot for visualising clusters of multivariate data.
  
      Andrews curves have the functional form:
  
      .. math::
          f(t) = \\frac{x_1}{\\sqrt{2}} + x_2 \\sin(t) + x_3 \\cos(t) +
          x_4 \\sin(2t) + x_5 \\cos(2t) + \\cdots
  
      Where :math:`x` coefficients correspond to the values of each dimension
      and :math:`t` is linearly spaced between :math:`-\\pi` and :math:`+\\pi`.
      Each row of frame then corresponds to a single curve.
  
      Parameters
      ----------
      frame : DataFrame
          Data to be plotted, preferably normalized to (0.0, 1.0).
      class_column : label
          Name of the column containing class names.
      ax : axes object, default None
          Axes to use.
      samples : int
          Number of points to plot in each curve.
      color : str, list[str] or tuple[str], optional
          Colors to use for the different classes. Colors can be strings
          or 3-element floating point RGB values.
      colormap : str or matplotlib colormap object, default None
          Colormap to select colors from. If a string, load colormap with that
          name from matplotlib.
      **kwargs
          Options to pass to matplotlib plotting method.
  
      Returns
      -------
      :class:`matplotlib.axes.Axes`
  
      Examples
      --------
  
      .. plot::
          :context: close-figs
  
          >>> df = pd.read_csv(
          ...     'https://raw.githubusercontent.com/pandas-dev/'
          ...     'pandas/main/pandas/tests/io/data/csv/iris.csv'
          ... )
          >>> pd.plotting.andrews_curves(df, 'Name')
          <AxesSubplot: title={'center': 'width'}>
      """
      plot_backend = _get_plot_backend("matplotlib")
      return plot_backend.andrews_curves(
          frame=frame,
          class_column=class_column,
          ax=ax,
          samples=samples,
          color=color,
          colormap=colormap,
          **kwargs,
      )
  
imports: from __future__ import annotations
  from contextlib import contextmanager
  from typing import TYPE_CHECKING, Generator
  from pandas.plotting._core import _get_plot_backend
label_desc: Use image node_id 6 for calling a global function with example usage: andrews_curves(frame, class_column, ax, samples, color, colormap) and returns: plot_backend