class_docstr: Plot functions for categorical x & y axes:
  
  Methods:
      - heatmap
class_docstr_tok: ["Plot","functions","for","categorical","x","&","y","axes",":","Methods",":","-","heatmap"]
docstr: Heatmap.
  
  Args:
      data_frame (pandas.DataFrame): Data source for the plot.
      x_column (str): Column name to plot on the x axis.
      y_column (str): Column name to plot on the y axis.
      color_column (str): Column name of numerical type to plot on
          the color dimension.
      text_column (str or None): Column name of the text labels.
      color_palette (str, chartify.ColorPalette): Color palette to
          apply to the heatmap.
          See chartify.color_palettes.show() for available color palettes.
      reverse_color_order (bool): Reverse order of the color palette.
      text_color (str): Color name or hex value.
          See chartify.color_palettes.show() for available color names.
      text_format: Python string formatting to apply to the text labels.
      color_value_min (float): Minimum value for the color palette.
          If None, will default to the min value of the
          color_column dimension.
      color_value_max (float): Maximum value for the color palette.
          If None, will default to the max value of the
          color_column dimension.
      color_value_range (int): The size of the range of colors in
          the color palette.
          A larger color range will result in greater variation
          among the cell colors.
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code: def heatmap(
      self,
      data_frame,
      x_column,
      y_column,
      color_column,
      text_column=None,
      color_palette="RdBu",
      reverse_color_order=False,
      text_color="white",
      text_format="{:,.2f}",
      color_value_min=None,
      color_value_max=None,
      color_value_range=100,
  ):
      """Heatmap.
  
      Args:
          data_frame (pandas.DataFrame): Data source for the plot.
          x_column (str): Column name to plot on the x axis.
          y_column (str): Column name to plot on the y axis.
          color_column (str): Column name of numerical type to plot on
              the color dimension.
          text_column (str or None): Column name of the text labels.
          color_palette (str, chartify.ColorPalette): Color palette to
              apply to the heatmap.
              See chartify.color_palettes.show() for available color palettes.
          reverse_color_order (bool): Reverse order of the color palette.
          text_color (str): Color name or hex value.
              See chartify.color_palettes.show() for available color names.
          text_format: Python string formatting to apply to the text labels.
          color_value_min (float): Minimum value for the color palette.
              If None, will default to the min value of the
              color_column dimension.
          color_value_max (float): Maximum value for the color palette.
              If None, will default to the max value of the
              color_column dimension.
          color_value_range (int): The size of the range of colors in
              the color palette.
              A larger color range will result in greater variation
              among the cell colors.
      """
      # Cast all categorical columns to strings
      # Plotting functions will break with non-str types.
      type_map = {column: str for column in [x_column, y_column]}
      self._chart.figure.x_range.factors = (
          data_frame[x_column].astype(str).unique()
      )
      self._chart.figure.y_range.factors = (
          data_frame[y_column].astype(str).unique()
      )
  
      cast_data = data_frame[[x_column, y_column, color_column]].astype(
          type_map
      )
  
      source = self._named_column_data_source(
          cast_data, series_name=None
      )
      if text_color:
          text_color = Color(text_color).get_hex_l()
      if isinstance(color_palette, str):
          color_palette = color_palettes[color_palette]
      if reverse_color_order:
          color_palette = color_palette[::-1]
      color_palette = color_palette.expand_palette(color_value_range)
      color_palette = [c.get_hex_l() for c in color_palette.colors]
  
      # If not specified set the min and max value based on the data.
      if not color_value_min:
          color_value_min = data_frame[color_column].min()
      if not color_value_max:
          color_value_max = data_frame[color_column].max()
      mapper = bokeh.models.LinearColorMapper(
          palette=color_palette,
          low=color_value_min,
          high=color_value_max,
      )
      self._chart.figure.rect(
          source=source,
          x=x_column,
          y=y_column,
          fill_color={"field": color_column, "transform": mapper},
          width=1,
          height=1,
          dilate=True,
          line_alpha=0,
      )
  
      if text_column:
          text_font = self._chart.style._get_settings(
              "text_callout_and_plot"
          )["font"]
          formatted_text = data_frame[text_column].map(
              text_format.format
          )
          source.add(formatted_text, "formatted_text")
          self._chart.figure.text(
              text="formatted_text",
              x=x_column,
              y=y_column,
              source=source,
              text_align="center",
              text_baseline="middle",
              text_color=text_color,
              text_font=text_font,
          )
      return self._chart
  
imports: import bokeh
  import pandas
  import numpy
  from chartify._core.colors import Color, color_palettes
  from chartify._core.axes import NumericalYMixin, NumericalXMixin
  from scipy.stats import gaussian_kde
label_desc: Use image node_id 1 for calling the PlotCategoricalXY obj's underlying member method code with example usage: obj.heatmap(data_frame, x_column, y_column, color_column, text_column, color_palette, reverse_color_order, text_color, text_format, color_value_min, color_value_max, color_value_range) and returns: self