text: import warnings
  import itertools
  from copy import copy
  from functools import partial
  from collections.abc import Iterable, Sequence, Mapping
  from numbers import Number
  from datetime import datetime
  
  import numpy as np
  import pandas as pd
  import matplotlib as mpl
  
  from ._decorators import (
      share_init_params_with_map,
  )
  from .palettes import (
      QUAL_PALETTES,
      color_palette,
      cubehelix_palette,
      _parse_cubehelix_args,
  )
  from .utils import (
      get_color_cycle,
      remove_na,
  )
  
  
  class SemanticMapping:
      """Base class for mapping data values to plot attributes."""
  
      # -- Default attributes that all SemanticMapping subclasses must set
  
      # Whether the mapping is numeric, categorical, or datetime
      map_type = None
  
      # Ordered list of unique values in the input data
      levels = None
  
      # A mapping from the data values to corresponding plot attributes
      lookup_table = None
  
      def __init__(self, plotter):
  
          # TODO Putting this here so we can continue to use a lot of the
          # logic that's built into the library, but the idea of this class
          # is to move towards semantic mappings that are agnositic about the
          # kind of plot they're going to be used to draw.
          # Fully achieving that is going to take some thinking.
          self.plotter = plotter
  
      def map(cls, plotter, *args, **kwargs):
          # This method is assigned the __init__ docstring
          method_name = "_{}_map".format(cls.__name__[:-7].lower())
          setattr(plotter, method_name, cls(plotter, *args, **kwargs))
          return plotter
  
      def _lookup_single(self, key):
          """Apply the mapping to a single data value."""
          return self.lookup_table[key]
  
      def __call__(self, key, *args, **kwargs):
          """Get the attribute(s) values for the data key."""
          if isinstance(key, (list, np.ndarray, pd.Series)):
              return [self._lookup_single(k, *args, **kwargs) for k in key]
          else:
              return self._lookup_single(key, *args, **kwargs)
  
  
  @share_init_params_with_map
  class HueMapping(SemanticMapping):
      """Mapping that sets artist colors according to data values."""
      # A specification of the colors that should appear in the plot
      palette = None
  
      # An object that normalizes data values to [0, 1] range for color mapping
      norm = None
  
      # A continuous colormap object for interpolating in a numeric context
      cmap = None
  
      def __init__(
          self, plotter, palette=None, order=None, norm=None,
      ):
          """Map the levels of the `hue` variable to distinct colors.
  
          Parameters
          ----------
          # TODO add generic parameters
  
          """
          super().__init__(plotter)
  
          data = plotter.plot_data["hue"]
  
          if data.notna().any():
  
              map_type = self.infer_map_type(
                  palette, norm, plotter.input_format, plotter.var_types["hue"]
              )
  
              # Our goal is to end up with a dictionary mapping every unique
              # value in `data` to a color. We will also keep track of the
              # metadata about this mapping we will need for, e.g., a legend
  
              # --- Option 1: numeric mapping with a matplotlib colormap
  
              if map_type == "numeric":
  
                  data = pd.to_numeric(data)
                  levels, lookup_table, norm, cmap = self.numeric_mapping(
                      data, palette, norm,
                  )
  
              # --- Option 2: categorical mapping using seaborn palette
  
              elif map_type == "categorical":
  
                  cmap = norm = None
                  levels, lookup_table = self.categorical_mapping(
                      data, palette, order,
                  )
  
              # --- Option 3: datetime mapping
  
              else:
                  # TODO this needs actual implementation
                  cmap = norm = None
                  levels, lookup_table = self.categorical_mapping(
                      # Casting data to list to handle differences in the way
                      # pandas and numpy represent datetime64 data
                      list(data), palette, order,
                  )
  
              self.map_type = map_type
              self.lookup_table = lookup_table
              self.palette = palette
              self.levels = levels
              self.norm = norm
              self.cmap = cmap
  
      def _lookup_single(self, key):
          """Get the color for a single value, using colormap to interpolate."""
          try:
              # Use a value that's in the original data vector
              value = self.lookup_table[key]
          except KeyError:
              # Use the colormap to interpolate between existing datapoints
              # (e.g. in the context of making a continuous legend)
              normed = self.norm(key)
              if np.ma.is_masked(normed):
                  normed = np.nan
              value = self.cmap(normed)
          return value
  
      def infer_map_type(self, palette, norm, input_format, var_type):
          """Determine how to implement the mapping."""
          if palette in QUAL_PALETTES:
              map_type = "categorical"
          elif norm is not None:
              map_type = "numeric"
          elif isinstance(palette, (dict, list)):
              map_type = "categorical"
          elif input_format == "wide":
              map_type = "categorical"
          else:
              map_type = var_type
  
          return map_type
  
      def categorical_mapping(self, data, palette, order):
          """Determine colors when the hue mapping is categorical."""
          # -- Identify the order and name of the levels
  
          levels = categorical_order(data, order)
          n_colors = len(levels)
  
          # -- Identify the set of colors to use
  
          if isinstance(palette, dict):
  
              missing = set(levels) - set(palette)
              if any(missing):
                  err = "The palette dictionary is missing keys: {}"
                  raise ValueError(err.format(missing))
  
              lookup_table = palette
  
          else:
  
              if palette is None:
                  if n_colors <= len(get_color_cycle()):
                      colors = color_palette(None, n_colors)
                  else:
                      colors = color_palette("husl", n_colors)
              elif isinstance(palette, list):
                  if len(palette) != n_colors:
                      err = "The palette list has the wrong number of colors."
                      raise ValueError(err)
                  colors = palette
              else:
                  colors = color_palette(palette, n_colors)
  
              lookup_table = dict(zip(levels, colors))
  
          return levels, lookup_table
  
      def numeric_mapping(self, data, palette, norm):
          """Determine colors when the hue variable is quantitative."""
          if isinstance(palette, dict):
  
              # The presence of a norm object overrides a dictionary of hues
              # in specifying a numeric mapping, so we need to process it here.
              levels = list(sorted(palette))
              colors = [palette[k] for k in sorted(palette)]
              cmap = mpl.colors.ListedColormap(colors)
              lookup_table = palette.copy()
  
          else:
  
              # The levels are the sorted unique values in the data
              levels = list(np.sort(remove_na(data.unique())))
  
              # --- Sort out the colormap to use from the palette argument
  
              # Default numeric palette is our default cubehelix palette
              # TODO do we want to do something complicated to ensure contrast?
              palette = "ch:" if palette is None else palette
  
              if isinstance(palette, mpl.colors.Colormap):
                  cmap = palette
              elif str(palette).startswith("ch:"):
                  args, kwargs = _parse_cubehelix_args(palette)
                  cmap = cubehelix_palette(0, *args, as_cmap=True, **kwargs)
              else:
                  try:
                      cmap = mpl.cm.get_cmap(palette)
                  except (ValueError, TypeError):
                      err = "Palette {} not understood"
                      raise ValueError(err)
  
              # Now sort out the data normalization
              if norm is None:
                  norm = mpl.colors.Normalize()
              elif isinstance(norm, tuple):
                  norm = mpl.colors.Normalize(*norm)
              elif not isinstance(norm, mpl.colors.Normalize):
                  err = "``hue_norm`` must be None, tuple, or Normalize object."
                  raise ValueError(err)
  
              if not norm.scaled():
                  norm(np.asarray(data.dropna()))
  
              lookup_table = dict(zip(levels, cmap(norm(levels))))
  
          return levels, lookup_table, norm, cmap
  
  
  @share_init_params_with_map
  class SizeMapping(SemanticMapping):
      """Mapping that sets artist sizes according to data values."""
      # An object that normalizes data values to [0, 1] range
      norm = None
  
      def __init__(
          self, plotter, sizes=None, order=None, norm=None,
      ):
          """Map the levels of the `size` variable to distinct values.
  
          Parameters
          ----------
          # TODO add generic parameters
  
          """
          super().__init__(plotter)
  
          data = plotter.plot_data["size"]
  
          if data.notna().any():
  
              map_type = self.infer_map_type(
                  norm, sizes, plotter.var_types["size"]
              )
  
              # --- Option 1: numeric mapping
  
              if map_type == "numeric":
  
                  levels, lookup_table, norm = self.numeric_mapping(
                      data, sizes, norm,
                  )
  
              # --- Option 2: categorical mapping
  
              elif map_type == "categorical":
  
                  levels, lookup_table = self.categorical_mapping(
                      data, sizes, order,
                  )
  
              # --- Option 3: datetime mapping
  
              # TODO this needs an actual implementation
              else:
  
                  levels, lookup_table = self.categorical_mapping(
                      # Casting data to list to handle differences in the way
                      # pandas and numpy represent datetime64 data
                      list(data), sizes, order,
                  )
  
              self.map_type = map_type
              self.levels = levels
              self.norm = norm
              self.sizes = sizes
              self.lookup_table = lookup_table
  
      def infer_map_type(self, norm, sizes, var_type):
  
          if norm is not None:
              map_type = "numeric"
          elif isinstance(sizes, (dict, list)):
              map_type = "categorical"
          else:
              map_type = var_type
  
          return map_type
  
      def _lookup_single(self, key):
  
          try:
              value = self.lookup_table[key]
          except KeyError:
              normed = self.norm(key)
              if np.ma.is_masked(normed):
                  normed = np.nan
              size_values = self.lookup_table.values()
              size_range = min(size_values), max(size_values)
              value = size_range[0] + normed * np.ptp(size_range)
          return value
  
      def categorical_mapping(self, data, sizes, order):
  
          levels = categorical_order(data, order)
  
          if isinstance(sizes, dict):
  
              # Dict inputs map existing data values to the size attribute
              missing = set(levels) - set(sizes)
              if any(missing):
                  err = f"Missing sizes for the following levels: {missing}"
                  raise ValueError(err)
              lookup_table = sizes.copy()
  
          elif isinstance(sizes, list):
  
              # List inputs give size values in the same order as the levels
              if len(sizes) != len(levels):
                  err = "The `sizes` list has the wrong number of values."
                  raise ValueError(err)
  
              lookup_table = dict(zip(levels, sizes))
  
          else:
  
              if isinstance(sizes, tuple):
  
                  # Tuple input sets the min, max size values
                  if len(sizes) != 2:
                      err = "A `sizes` tuple must have only 2 values"
                      raise ValueError(err)
  
              elif sizes is not None:
  
                  err = f"Value for `sizes` not understood: {sizes}"
                  raise ValueError(err)
  
              else:
  
                  # Otherwise, we need to get the min, max size values from
                  # the plotter object we are attached to.
  
                  # TODO this is going to cause us trouble later, because we
                  # want to restructure things so that the plotter is generic
                  # across the visual representation of the data. But at this
                  # point, we don't know the visual representation. Likely we
                  # want to change the logic of this Mapping so that it gives
                  # points on a nornalized range that then gets unnormalized
                  # when we know what we're drawing. But given the way the
                  # package works now, this way is cleanest.
                  sizes = self.plotter._default_size_range
  
              # For categorical sizes, use regularly-spaced linear steps
              # between the minimum and maximum sizes
              sizes = np.linspace(*sizes, len(levels))
              lookup_table = dict(zip(levels, sizes))
  
          return levels, lookup_table
  
      def numeric_mapping(self, data, sizes, norm):
  
          if isinstance(sizes, dict):
              # The presence of a norm object overrides a dictionary of sizes
              # in specifying a numeric mapping, so we need to process it
              # dictionary here
              levels = list(np.sort(list(sizes)))
              size_values = sizes.values()
              size_range = min(size_values), max(size_values)
  
          else:
  
              # The levels here will be the unique values in the data
              levels = list(np.sort(remove_na(data.unique())))
  
              if isinstance(sizes, tuple):
  
                  # For numeric inputs, the size can be parametrized by
                  # the minimum and maximum artist values to map to. The
                  # norm object that gets set up next specifies how to
                  # do the mapping.
  
                  if len(sizes) != 2:
                      err = "A `sizes` tuple must have only 2 values"
                      raise ValueError(err)
  
                  size_range = sizes
  
              elif sizes is not None:
  
                  err = f"Value for `sizes` not understood: {sizes}"
                  raise ValueError(err)
  
              else:
  
                  # When not provided, we get the size range from the plotter
                  # object we are attached to. See the note in the categorical
                  # method about how this is suboptimal for future development.:
                  size_range = self.plotter._default_size_range
  
          # Now that we know the minimum and maximum sizes that will get drawn,
          # we need to map the data values that we have into that range. We will
          # use a matplotlib Normalize class, which is typically used for numeric
          # color mapping but works fine here too. It takes data values and maps
          # them into a [0, 1] interval, potentially nonlinear-ly.
  
          if norm is None:
              # Default is a linear function between the min and max data values
              norm = mpl.colors.Normalize()
          elif isinstance(norm, tuple):
              # It is also possible to give different limits in data space
              norm = mpl.colors.Normalize(*norm)
          elif not isinstance(norm, mpl.colors.Normalize):
              err = f"Value for size `norm` parameter not understood: {norm}"
              raise ValueError(err)
          else:
              # If provided with Normalize object, copy it so we can modify
              norm = copy(norm)
  
          # Set the mapping so all output values are in [0, 1]
          norm.clip = True
  
          # If the input range is not set, use the full range of the data
          if not norm.scaled():
              norm(levels)
  
          # Map from data values to [0, 1] range
          sizes_scaled = norm(levels)
  
          # Now map from the scaled range into the artist units
          if isinstance(sizes, dict):
              lookup_table = sizes
          else:
              lo, hi = size_range
              sizes = lo + sizes_scaled * (hi - lo)
              lookup_table = dict(zip(levels, sizes))
  
          return levels, lookup_table, norm
  
  
  @share_init_params_with_map
  class StyleMapping(SemanticMapping):
      """Mapping that sets artist style according to data values."""
  
      # Style mapping is always treated as categorical
      map_type = "categorical"
  
      def __init__(
          self, plotter, markers=None, dashes=None, order=None,
      ):
          """Map the levels of the `style` variable to distinct values.
  
          Parameters
          ----------
          # TODO add generic parameters
  
          """
          super().__init__(plotter)
  
          data = plotter.plot_data["style"]
  
          if data.notna().any():
  
              # Cast to list to handle numpy/pandas datetime quirks
              if variable_type(data) == "datetime":
                  data = list(data)
  
              # Find ordered unique values
              levels = categorical_order(data, order)
  
              markers = self._map_attributes(
                  markers, levels, unique_markers(len(levels)), "markers",
              )
              dashes = self._map_attributes(
                  dashes, levels, unique_dashes(len(levels)), "dashes",
              )
  
              # Build the paths matplotlib will use to draw the markers
              paths = {}
              filled_markers = []
              for k, m in markers.items():
                  if not isinstance(m, mpl.markers.MarkerStyle):
                      m = mpl.markers.MarkerStyle(m)
                  paths[k] = m.get_path().transformed(m.get_transform())
                  filled_markers.append(m.is_filled())
  
              # Mixture of filled and unfilled markers will show line art markers
              # in the edge color, which defaults to white. This can be handled,
              # but there would be additional complexity with specifying the
              # weight of the line art markers without overwhelming the filled
              # ones with the edges. So for now, we will disallow mixtures.
              if any(filled_markers) and not all(filled_markers):
                  err = "Filled and line art markers cannot be mixed"
                  raise ValueError(err)
  
              lookup_table = {}
              for key in levels:
                  lookup_table[key] = {}
                  if markers:
                      lookup_table[key]["marker"] = markers[key]
                      lookup_table[key]["path"] = paths[key]
                  if dashes:
                      lookup_table[key]["dashes"] = dashes[key]
  
              self.levels = levels
              self.lookup_table = lookup_table
  
      def _lookup_single(self, key, attr=None):
          """Get attribute(s) for a given data point."""
          if attr is None:
              value = self.lookup_table[key]
          else:
              value = self.lookup_table[key][attr]
          return value
  
      def _map_attributes(self, arg, levels, defaults, attr):
          """Handle the specification for a given style attribute."""
          if arg is True:
              lookup_table = dict(zip(levels, defaults))
          elif isinstance(arg, dict):
              missing = set(levels) - set(arg)
              if missing:
                  err = f"These `{attr}` levels are missing values: {missing}"
                  raise ValueError(err)
              lookup_table = arg
          elif isinstance(arg, Sequence):
              if len(levels) != len(arg):
                  err = f"The `{attr}` argument has the wrong number of values"
                  raise ValueError(err)
              lookup_table = dict(zip(levels, arg))
          elif arg:
              err = f"This `{attr}` argument was not understood: {arg}"
              raise ValueError(err)
          else:
              lookup_table = {}
  
          return lookup_table
  
  
  # =========================================================================== #
  
  
  class VectorPlotter:
      """Base class for objects underlying *plot functions."""
  
      _semantic_mappings = {
          "hue": HueMapping,
          "size": SizeMapping,
          "style": StyleMapping,
      }
  
      # TODO units is another example of a non-mapping "semantic"
      # we need a general name for this and separate handling
      semantics = "x", "y", "hue", "size", "style", "units"
      wide_structure = {
          "x": "index", "y": "values", "hue": "columns", "style": "columns",
      }
      flat_structure = {"x": "index", "y": "values"}
  
      _default_size_range = 1, 2  # Unused but needed in tests, ugh
  
      def __init__(self, data=None, variables={}):
  
          self.assign_variables(data, variables)
  
          for var, cls in self._semantic_mappings.items():
              if var in self.semantics:
  
                  # Create the mapping function
                  map_func = partial(cls.map, plotter=self)
                  setattr(self, f"map_{var}", map_func)
  
                  # Call the mapping function to initialize with default values
                  getattr(self, f"map_{var}")()
  
      @classmethod
      def get_semantics(cls, kwargs):
          """Subset a dictionary` arguments with known semantic variables."""
          return {k: kwargs[k] for k in cls.semantics}
  
      def assign_variables(self, data=None, variables={}):
          """Define plot variables, optionally using lookup from `data`."""
          x = variables.get("x", None)
          y = variables.get("y", None)
  
          if x is None and y is None:
              self.input_format = "wide"
              plot_data, variables = self._assign_variables_wideform(
                  data, **variables,
              )
          else:
              self.input_format = "long"
              plot_data, variables = self._assign_variables_longform(
                  data, **variables,
              )
  
          self.plot_data = plot_data
          self.variables = variables
          self.var_types = {
              v: variable_type(
                  plot_data[v],
                  boolean_type="numeric" if v in "xy" else "categorical"
              )
              for v in variables
          }
  
          return self
  
      def _assign_variables_wideform(self, data=None, **kwargs):
          """Define plot variables given wide-form data.
  
          Parameters
          ----------
          data : flat vector or collection of vectors
              Data can be a vector or mapping that is coerceable to a Series
              or a sequence- or mapping-based collection of such vectors, or a
              rectangular numpy array, or a Pandas DataFrame.
          kwargs : variable -> data mappings
              Behavior with keyword arguments is currently undefined.
  
          Returns
          -------
          plot_data : :class:`pandas.DataFrame`
              Long-form data object mapping seaborn variables (x, y, hue, ...)
              to data vectors.
          variables : dict
              Keys are defined seaborn variables; values are names inferred from
              the inputs (or None when no name can be determined).
  
          """
          # TODO raise here if any kwarg values are not None,
          # # if we decide for "structure-only" wide API
  
          # First, determine if the data object actually has any data in it
          empty = data is None or not len(data)
  
          # Then, determine if we have "flat" data (a single vector)
          if isinstance(data, dict):
              values = data.values()
          else:
              values = np.atleast_1d(data)
          flat = not any(
              isinstance(v, Iterable) and not isinstance(v, (str, bytes))
              for v in values
          )
  
          if empty:
  
              # Make an object with the structure of plot_data, but empty
              plot_data = pd.DataFrame(columns=self.semantics)
              variables = {}
  
          elif flat:
  
              # Handle flat data by converting to pandas Series and using the
              # index and/or values to define x and/or y
              # (Could be accomplished with a more general to_series() interface)
              flat_data = pd.Series(data).copy()
              names = {
                  "values": flat_data.name,
                  "index": flat_data.index.name
              }
  
              plot_data = {}
              variables = {}
  
              for var in ["x", "y"]:
                  if var in self.flat_structure:
                      attr = self.flat_structure[var]
                      plot_data[var] = getattr(flat_data, attr)
                      variables[var] = names[self.flat_structure[var]]
  
              plot_data = pd.DataFrame(plot_data).reindex(columns=self.semantics)
  
          else:
  
              # Otherwise assume we have some collection of vectors.
  
              # Handle Python sequences such that entries end up in the columns,
              # not in the rows, of the intermediate wide DataFrame.
              # One way to accomplish this is to convert to a dict of Series.
              if isinstance(data, Sequence):
                  data_dict = {}
                  for i, var in enumerate(data):
                      key = getattr(var, "name", i)
                      # TODO is there a safer/more generic way to ensure Series?
                      # sort of like np.asarray, but for pandas?
                      data_dict[key] = pd.Series(var)
  
                  data = data_dict
  
              # Pandas requires that dict values either be Series objects
              # or all have the same length, but we want to allow "ragged" inputs
              if isinstance(data, Mapping):
                  data = {key: pd.Series(val) for key, val in data.items()}
  
              # Otherwise, delegate to the pandas DataFrame constructor
              # This is where we'd prefer to use a general interface that says
              # "give me this data as a pandas DataFrame", so we can accept
              # DataFrame objects from other libraries
              wide_data = pd.DataFrame(data, copy=True)
  
              # At this point we should reduce the dataframe to numeric cols
              numeric_cols = wide_data.apply(variable_type) == "numeric"
              wide_data = wide_data.loc[:, numeric_cols]
  
              # Now melt the data to long form
              melt_kws = {"var_name": "columns", "value_name": "values"}
              if "index" in self.wide_structure.values():
                  melt_kws["id_vars"] = "index"
                  wide_data["index"] = wide_data.index.to_series()
              plot_data = wide_data.melt(**melt_kws)
  
              # Assign names corresponding to plot semantics
              for var, attr in self.wide_structure.items():
                  plot_data[var] = plot_data[attr]
              plot_data = plot_data.reindex(columns=self.semantics)
  
              # Define the variable names
              variables = {}
              for var, attr in self.wide_structure.items():
                  obj = getattr(wide_data, attr)
                  variables[var] = getattr(obj, "name", None)
  
          return plot_data, variables
  
      def _assign_variables_longform(self, data=None, **kwargs):
          """Define plot variables given long-form data and/or vector inputs.
  
          Parameters
          ----------
          data : dict-like collection of vectors
              Input data where variable names map to vector values.
          kwargs : variable -> data mappings
              Keys are seaborn variables (x, y, hue, ...) and values are vectors
              in any format that can construct a :class:`pandas.DataFrame` or
              names of columns or index levels in ``data``.
  
          Returns
          -------
          plot_data : :class:`pandas.DataFrame`
              Long-form data object mapping seaborn variables (x, y, hue, ...)
              to data vectors.
          variables : dict
              Keys are defined seaborn variables; values are names inferred from
              the inputs (or None when no name can be determined).
  
          Raises
          ------
          ValueError
              When variables are strings that don't appear in ``data``.
  
          """
          plot_data = {}
          variables = {}
  
          # Data is optional; all variables can be defined as vectors
          if data is None:
              data = {}
  
          # TODO should we try a data.to_dict() or similar here to more
          # generally accept objects with that interface?
          # Note that dict(df) also works for pandas, and gives us what we
          # want, whereas DataFrame.to_dict() gives a nested dict instead of
          # a dict of series.
  
          # Variables can also be extraced from the index attribute
          # TODO is this the most general way to enable it?
          # There is no index.to_dict on multiindex, unfortunately
          try:
              index = data.index.to_frame()
          except AttributeError:
              index = {}
  
          # The caller will determine the order of variables in plot_data
          for key, val in kwargs.items():
  
              if isinstance(val, (str, bytes)):
                  # String inputs trigger __getitem__
                  if val in data:
                      # First try to get an entry in the data object
                      plot_data[key] = data[val]
                      variables[key] = val
                  elif val in index:
                      # Failing that, try to get an entry in the index object
                      plot_data[key] = index[val]
                      variables[key] = val
                  else:
                      # We don't know what this name means
                      err = f"Could not interpret input '{val}'"
                      raise ValueError(err)
  
              else:
  
                  # Otherwise, assume the value is itself a vector of data
                  # TODO check for 1D here or let pd.DataFrame raise?
                  plot_data[key] = val
                  # Try to infer the name of the variable
                  variables[key] = getattr(val, "name", None)
  
          # Construct a tidy plot DataFrame. This will convert a number of
          # types automatically, aligning on index in case of pandas objects
          plot_data = pd.DataFrame(plot_data, columns=self.semantics)
  
          # Reduce the variables dictionary to fields with valid data
          variables = {
              var: name
              for var, name in variables.items()
              if plot_data[var].notnull().any()
          }
  
          return plot_data, variables
  
      def _semantic_subsets(self, grouping_semantics, reverse=False):
          """Generator for getting subsets of data defined by semantic variables.
  
          Parameters
          ----------
          grouping_semantics : list of strings
              Semantic variables that define the subsets of data.
          reverse : bool, optional
              If True, reverse the order of iteration.
  
          Yields
          ------
          sub_vars : dict
              Keys are semantic names, values are the level of that semantic.
          sub_data : :class:`pandas.DataFrame`
              Subset of ``plot_data`` for this combination of semantic values.
  
          """
          if isinstance(grouping_semantics, str):
              grouping_semantics = [grouping_semantics]
  
          # Reduce to the semantics used in this plot
          grouping_semantics = [
              var for var in grouping_semantics if var in self.variables
          ]
  
          if grouping_semantics:
  
              grouped_data = self.plot_data.groupby(
                  grouping_semantics, sort=False, as_index=False
              )
  
              grouping_keys = []
              for var in grouping_semantics:
                  # TODO this is messy, add "semantic levels" property?
                  map_obj = getattr(self, f"_{var}_map")
                  grouping_keys.append(map_obj.levels)
  
              iter_keys = itertools.product(*grouping_keys)
              if reverse:
                  iter_keys = reversed(list(iter_keys))
  
              for key in iter_keys:
  
                  # Pandas fails with singleton tuple inputs
                  pd_key = key[0] if len(key) == 1 else key
  
                  try:
                      data_subset = grouped_data.get_group(pd_key)
                  except KeyError:
                      continue
  
                  yield dict(zip(grouping_semantics, key)), data_subset
  
          else:
  
              yield {}, self.plot_data
  
      def _add_axis_labels(self, ax, default_x="", default_y=""):
  
          if not ax.get_xlabel():
              ax.set_xlabel(self.variables.get("x", default_x))
          if not ax.get_ylabel():
              ax.set_ylabel(self.variables.get("y", default_y))
  
  
  def variable_type(vector, boolean_type="numeric"):
      """Determine whether a vector contains numeric, categorical, or dateime data.
  
      This function differs from the pandas typing API in two ways:
  
      - Python sequences or object-typed PyData objects are considered numeric if
        all of their entries are numeric.
      - String or mixed-type data are considered categorical even if not
        explicitly represented as a :class:pandas.api.types.CategoricalDtype`.
  
      Parameters
      ----------
      vector : :func:`pandas.Series`, :func:`numpy.ndarray`, or Python sequence
          Input data to test.
      binary_type : 'numeric' or 'categorical'
          Type to use for vectors containing only 0s and 1s (and NAs).
  
      Returns
      -------
      var_type : 'numeric', 'categorical', or 'datetime'
          Name identifying the type of data in the vector.
  
      """
      # Special-case all-na data, which is always "numeric"
      if pd.isna(vector).all():
          return "numeric"
  
      # Special-case binary/boolean data, allow caller to determine
      # This triggers a numpy warning when vector has strings/objects
      # https://github.com/numpy/numpy/issues/6784
      # Because we reduce with .all(), we are agnostic about whether the
      # comparison returns a scalar or vector, so we will ignore the warning.
      # It triggers a separate DeprecationWarning when the vector has datetimes:
      # https://github.com/numpy/numpy/issues/13548
      # This is considered a bug by numpy and will likely go away.
      with warnings.catch_warnings():
          warnings.simplefilter(
              action='ignore', category=(FutureWarning, DeprecationWarning)
          )
          if np.isin(vector, [0, 1, np.nan]).all():
              return boolean_type
  
      # Defer to positive pandas tests
      if pd.api.types.is_numeric_dtype(vector):
          return "numeric"
  
      if pd.api.types.is_categorical_dtype(vector):
          return "categorical"
  
      if pd.api.types.is_datetime64_dtype(vector):
          return "datetime"
  
      # --- If we get to here, we need to check the entries
  
      # Check for a collection where everything is a number
  
      def all_numeric(x):
          for x_i in x:
              if not isinstance(x_i, Number):
                  return False
          return True
  
      if all_numeric(vector):
          return "numeric"
  
      # Check for a collection where everything is a datetime
  
      def all_datetime(x):
          for x_i in x:
              if not isinstance(x_i, (datetime, np.datetime64)):
                  return False
          return True
  
      if all_datetime(vector):
          return "datetime"
  
      # Otherwise, our final fallback is to consider things categorical
  
      return "categorical"
  
  
  def infer_orient(x=None, y=None, orient=None, require_numeric=True):
      """Determine how the plot should be oriented based on the data.
  
      For historical reasons, the convention is to call a plot "horizontally"
      or "vertically" oriented based on the axis representing its dependent
      variable. Practically, this is used when determining the axis for
      numerical aggregation.
  
      Paramters
      ---------
      x, y : Vector data or None
          Positional data vectors for the plot.
      orient : string or None
          Specified orientation, which must start with "v" or "h" if not None.
      require_numeric : bool
          If set, raise when the implied dependent variable is not numeric.
  
      Returns
      -------
      orient : "v" or "h"
  
      Raises
      ------
      ValueError: When `orient` is not None and does not start with "h" or "v"
      TypeError: When dependant variable is not numeric, with `require_numeric`
  
      """
  
      x_type = None if x is None else variable_type(x)
      y_type = None if y is None else variable_type(y)
  
      nonnumeric_dv_error = "{} orientation requires numeric `{}` variable."
      single_var_warning = "{} orientation ignored with only `{}` specified."
  
      if x is None:
          if str(orient).startswith("h"):
              warnings.warn(single_var_warning.format("Horizontal", "y"))
          if require_numeric and y_type != "numeric":
              raise TypeError(nonnumeric_dv_error.format("Vertical", "y"))
          return "v"
  
      elif y is None:
          if str(orient).startswith("v"):
              warnings.warn(single_var_warning.format("Vertical", "x"))
          if require_numeric and x_type != "numeric":
              raise TypeError(nonnumeric_dv_error.format("Horizontal", "x"))
          return "h"
  
      elif str(orient).startswith("v"):
          if require_numeric and y_type != "numeric":
              raise TypeError(nonnumeric_dv_error.format("Vertical", "y"))
          return "v"
  
      elif str(orient).startswith("h"):
          if require_numeric and x_type != "numeric":
              raise TypeError(nonnumeric_dv_error.format("Horizontal", "x"))
          return "h"
  
      elif orient is not None:
          raise ValueError(f"Value for `orient` not understood: {orient}")
  
      elif x_type != "numeric" and y_type == "numeric":
          return "v"
  
      elif x_type == "numeric" and y_type != "numeric":
          return "h"
  
      elif require_numeric and "numeric" not in (x_type, y_type):
          err = "Neither the `x` nor `y` variable appears to be numeric."
          raise TypeError(err)
  
      else:
          return "v"
  
  
  def unique_dashes(n):
      """Build an arbitrarily long list of unique dash styles for lines.
  
      Parameters
      ----------
      n : int
          Number of unique dash specs to generate.
  
      Returns
      -------
      dashes : list of strings or tuples
          Valid arguments for the ``dashes`` parameter on
          :class:`matplotlib.lines.Line2D`. The first spec is a solid
          line (``""``), the remainder are sequences of long and short
          dashes.
  
      """
      # Start with dash specs that are well distinguishable
      dashes = [
          "",
          (4, 1.5),
          (1, 1),
          (3, 1.25, 1.5, 1.25),
          (5, 1, 1, 1),
      ]
  
      # Now programatically build as many as we need
      p = 3
      while len(dashes) < n:
  
          # Take combinations of long and short dashes
          a = itertools.combinations_with_replacement([3, 1.25], p)
          b = itertools.combinations_with_replacement([4, 1], p)
  
          # Interleave the combinations, reversing one of the streams
          segment_list = itertools.chain(*zip(
              list(a)[1:-1][::-1],
              list(b)[1:-1]
          ))
  
          # Now insert the gaps
          for segments in segment_list:
              gap = min(segments)
              spec = tuple(itertools.chain(*((seg, gap) for seg in segments)))
              dashes.append(spec)
  
          p += 1
  
      return dashes[:n]
  
  
  def unique_markers(n):
      """Build an arbitrarily long list of unique marker styles for points.
  
      Parameters
      ----------
      n : int
          Number of unique marker specs to generate.
  
      Returns
      -------
      markers : list of string or tuples
          Values for defining :class:`matplotlib.markers.MarkerStyle` objects.
          All markers will be filled.
  
      """
      # Start with marker specs that are well distinguishable
      markers = [
          "o",
          "X",
          (4, 0, 45),
          "P",
          (4, 0, 0),
          (4, 1, 0),
          "^",
          (4, 1, 45),
          "v",
      ]
  
      # Now generate more from regular polygons of increasing order
      s = 5
      while len(markers) < n:
          a = 360 / (s + 1) / 2
          markers.extend([
              (s + 1, 1, a),
              (s + 1, 0, a),
              (s, 1, 0),
              (s, 0, 0),
          ])
          s += 1
  
      # Convert to MarkerStyle object, using only exactly what we need
      # markers = [mpl.markers.MarkerStyle(m) for m in markers[:n]]
  
      return markers[:n]
  
  
  def categorical_order(vector, order=None):
      """Return a list of unique data values.
  
      Determine an ordered list of levels in ``values``.
  
      Parameters
      ----------
      vector : list, array, Categorical, or Series
          Vector of "categorical" values
      order : list-like, optional
          Desired order of category levels to override the order determined
          from the ``values`` object.
  
      Returns
      -------
      order : list
          Ordered list of category levels not including null values.
  
      """
      if order is None:
          if hasattr(vector, "categories"):
              order = vector.categories
          else:
              try:
                  order = vector.cat.categories
              except (TypeError, AttributeError):
  
                  try:
                      order = vector.unique()
                  except AttributeError:
                      order = pd.unique(vector)
  
                  if variable_type(vector) == "numeric":
                      order = np.sort(order)
  
          order = filter(pd.notnull, order)
      return list(order)
  