code: def create_choropleth(
      fips,
      values,
      scope=["usa"],
      binning_endpoints=None,
      colorscale=None,
      order=None,
      simplify_county=0.02,
      simplify_state=0.02,
      asp=None,
      show_hover=True,
      show_state_data=True,
      state_outline=None,
      county_outline=None,
      centroid_marker=None,
      round_legend_values=False,
      exponent_format=False,
      legend_title="",
      **layout_options,
  ):
      """
      **deprecated**, use instead
      :func:`plotly.express.choropleth` with custom GeoJSON.
  
      This function also requires `shapely`, `geopandas` and `plotly-geo` to be installed.
  
      Returns figure for county choropleth. Uses data from package_data.
  
      :param (list) fips: list of FIPS values which correspond to the con
          catination of state and county ids. An example is '01001'.
      :param (list) values: list of numbers/strings which correspond to the
          fips list. These are the values that will determine how the counties
          are colored.
      :param (list) scope: list of states and/or states abbreviations. Fits
          all states in the camera tightly. Selecting ['usa'] is the equivalent
          of appending all 50 states into your scope list. Selecting only 'usa'
          does not include 'Alaska', 'Puerto Rico', 'American Samoa',
          'Commonwealth of the Northern Mariana Islands', 'Guam',
          'United States Virgin Islands'. These must be added manually to the
          list.
          Default = ['usa']
      :param (list) binning_endpoints: ascending numbers which implicitly define
          real number intervals which are used as bins. The colorscale used must
          have the same number of colors as the number of bins and this will
          result in a categorical colormap.
      :param (list) colorscale: a list of colors with length equal to the
          number of categories of colors. The length must match either all
          unique numbers in the 'values' list or if endpoints is being used, the
          number of categories created by the endpoints.\n
          For example, if binning_endpoints = [4, 6, 8], then there are 4 bins:
          [-inf, 4), [4, 6), [6, 8), [8, inf)
      :param (list) order: a list of the unique categories (numbers/bins) in any
          desired order. This is helpful if you want to order string values to
          a chosen colorscale.
      :param (float) simplify_county: determines the simplification factor
          for the counties. The larger the number, the fewer vertices and edges
          each polygon has. See
          http://toblerity.org/shapely/manual.html#object.simplify for more
          information.
          Default = 0.02
      :param (float) simplify_state: simplifies the state outline polygon.
          See http://toblerity.org/shapely/manual.html#object.simplify for more
          information.
          Default = 0.02
      :param (float) asp: the width-to-height aspect ratio for the camera.
          Default = 2.5
      :param (bool) show_hover: show county hover and centroid info
      :param (bool) show_state_data: reveals state boundary lines
      :param (dict) state_outline: dict of attributes of the state outline
          including width and color. See
          https://plot.ly/python/reference/#scatter-marker-line for all valid
          params
      :param (dict) county_outline: dict of attributes of the county outline
          including width and color. See
          https://plot.ly/python/reference/#scatter-marker-line for all valid
          params
      :param (dict) centroid_marker: dict of attributes of the centroid marker.
          The centroid markers are invisible by default and appear visible on
          selection. See https://plot.ly/python/reference/#scatter-marker for
          all valid params
      :param (bool) round_legend_values: automatically round the numbers that
          appear in the legend to the nearest integer.
          Default = False
      :param (bool) exponent_format: if set to True, puts numbers in the K, M,
          B number format. For example 4000.0 becomes 4.0K
          Default = False
      :param (str) legend_title: title that appears above the legend
      :param **layout_options: a **kwargs argument for all layout parameters
  
  
      Example 1: Florida::
  
          import plotly.plotly as py
          import plotly.figure_factory as ff
  
          import numpy as np
          import pandas as pd
  
          df_sample = pd.read_csv(
              'https://raw.githubusercontent.com/plotly/datasets/master/minoritymajority.csv'
          )
          df_sample_r = df_sample[df_sample['STNAME'] == 'Florida']
  
          values = df_sample_r['TOT_POP'].tolist()
          fips = df_sample_r['FIPS'].tolist()
  
          binning_endpoints = list(np.mgrid[min(values):max(values):4j])
          colorscale = ["#030512","#1d1d3b","#323268","#3d4b94","#3e6ab0",
                      "#4989bc","#60a7c7","#85c5d3","#b7e0e4","#eafcfd"]
          fig = ff.create_choropleth(
              fips=fips, values=values, scope=['Florida'], show_state_data=True,
              colorscale=colorscale, binning_endpoints=binning_endpoints,
              round_legend_values=True, plot_bgcolor='rgb(229,229,229)',
              paper_bgcolor='rgb(229,229,229)', legend_title='Florida Population',
              county_outline={'color': 'rgb(255,255,255)', 'width': 0.5},
              exponent_format=True,
          )
  
      Example 2: New England::
  
          import plotly.figure_factory as ff
  
          import pandas as pd
  
          NE_states = ['Connecticut', 'Maine', 'Massachusetts',
                      'New Hampshire', 'Rhode Island']
          df_sample = pd.read_csv(
              'https://raw.githubusercontent.com/plotly/datasets/master/minoritymajority.csv'
          )
          df_sample_r = df_sample[df_sample['STNAME'].isin(NE_states)]
          colorscale = ['rgb(68.0, 1.0, 84.0)',
          'rgb(66.0, 64.0, 134.0)',
          'rgb(38.0, 130.0, 142.0)',
          'rgb(63.0, 188.0, 115.0)',
          'rgb(216.0, 226.0, 25.0)']
  
          values = df_sample_r['TOT_POP'].tolist()
          fips = df_sample_r['FIPS'].tolist()
          fig = ff.create_choropleth(
              fips=fips, values=values, scope=NE_states, show_state_data=True
          )
          fig.show()
  
      Example 3: California and Surrounding States::
  
          import plotly.figure_factory as ff
  
          import pandas as pd
  
          df_sample = pd.read_csv(
              'https://raw.githubusercontent.com/plotly/datasets/master/minoritymajority.csv'
          )
          df_sample_r = df_sample[df_sample['STNAME'] == 'California']
  
          values = df_sample_r['TOT_POP'].tolist()
          fips = df_sample_r['FIPS'].tolist()
  
          colorscale = [
              'rgb(193, 193, 193)',
              'rgb(239,239,239)',
              'rgb(195, 196, 222)',
              'rgb(144,148,194)',
              'rgb(101,104,168)',
              'rgb(65, 53, 132)'
          ]
  
          fig = ff.create_choropleth(
              fips=fips, values=values, colorscale=colorscale,
              scope=['CA', 'AZ', 'Nevada', 'Oregon', ' Idaho'],
              binning_endpoints=[14348, 63983, 134827, 426762, 2081313],
              county_outline={'color': 'rgb(255,255,255)', 'width': 0.5},
              legend_title='California Counties',
              title='California and Nearby States'
          )
          fig.show()
  
      Example 4: USA::
  
          import plotly.figure_factory as ff
  
          import numpy as np
          import pandas as pd
  
          df_sample = pd.read_csv(
              'https://raw.githubusercontent.com/plotly/datasets/master/laucnty16.csv'
          )
          df_sample['State FIPS Code'] = df_sample['State FIPS Code'].apply(
              lambda x: str(x).zfill(2)
          )
          df_sample['County FIPS Code'] = df_sample['County FIPS Code'].apply(
              lambda x: str(x).zfill(3)
          )
          df_sample['FIPS'] = (
              df_sample['State FIPS Code'] + df_sample['County FIPS Code']
          )
  
          binning_endpoints = list(np.linspace(1, 12, len(colorscale) - 1))
          colorscale = ["#f7fbff", "#ebf3fb", "#deebf7", "#d2e3f3", "#c6dbef",
                      "#b3d2e9", "#9ecae1", "#85bcdb", "#6baed6", "#57a0ce",
                      "#4292c6", "#3082be", "#2171b5", "#1361a9", "#08519c",
                      "#0b4083","#08306b"]
          fips = df_sample['FIPS']
          values = df_sample['Unemployment Rate (%)']
          fig = ff.create_choropleth(
              fips=fips, values=values, scope=['usa'],
              binning_endpoints=binning_endpoints, colorscale=colorscale,
              show_hover=True, centroid_marker={'opacity': 0},
              asp=2.9, title='USA by Unemployment %',
              legend_title='Unemployment %'
          )
          fig.show()
      """
      # ensure optional modules imported
      if not _plotly_geo:
          raise ValueError(
              """
  The create_choropleth figure factory requires the plotly-geo package.
  Install using pip with:
  
  $ pip install plotly-geo
  
  Or, install using conda with
  
  $ conda install -c plotly plotly-geo
  """
          )
  
      if not gp or not shapefile or not shapely:
          raise ImportError(
              "geopandas, pyshp and shapely must be installed for this figure "
              "factory.\n\nRun the following commands to install the correct "
              "versions of the following modules:\n\n"
              "```\n"
              "$ pip install geopandas==0.3.0\n"
              "$ pip install pyshp==1.2.10\n"
              "$ pip install shapely==1.6.3\n"
              "```\n"
              "If you are using Windows, follow this post to properly "
              "install geopandas and dependencies:"
              "http://geoffboeing.com/2014/09/using-geopandas-windows/\n\n"
              "If you are using Anaconda, do not use PIP to install the "
              "packages above. Instead use conda to install them:\n\n"
              "```\n"
              "$ conda install plotly\n"
              "$ conda install geopandas\n"
              "```"
          )
  
      df, df_state = _create_us_counties_df(
          st_to_state_name_dict, state_to_st_dict
      )
  
      fips_polygon_map = dict(
          zip(df["FIPS"].tolist(), df["geometry"].tolist())
      )
  
      if not state_outline:
          state_outline = {"color": "rgb(240, 240, 240)", "width": 1}
      if not county_outline:
          county_outline = {"color": "rgb(0, 0, 0)", "width": 0}
      if not centroid_marker:
          centroid_marker = {"size": 3, "color": "white", "opacity": 1}
  
      # ensure centroid markers appear on selection
      if "opacity" not in centroid_marker:
          centroid_marker.update({"opacity": 1})
  
      if len(fips) != len(values):
          raise PlotlyError("fips and values must be the same length")
  
      # make fips, values into lists
      if isinstance(fips, pd.core.series.Series):
          fips = fips.tolist()
      if isinstance(values, pd.core.series.Series):
          values = values.tolist()
  
      # make fips numeric
      fips = map(lambda x: int(x), fips)
  
      if binning_endpoints:
          intervals = utils.endpts_to_intervals(binning_endpoints)
          LEVELS = _intervals_as_labels(
              intervals, round_legend_values, exponent_format
          )
      else:
          if not order:
              LEVELS = sorted(list(set(values)))
          else:
              # check if order is permutation
              # of unique color col values
              same_sets = sorted(list(set(values))) == set(order)
              no_duplicates = not any(order.count(x) > 1 for x in order)
              if same_sets and no_duplicates:
                  LEVELS = order
              else:
                  raise PlotlyError(
                      "if you are using a custom order of unique values from "
                      "your color column, you must: have all the unique values "
                      "in your order and have no duplicate items"
                  )
  
      if not colorscale:
          colorscale = []
          viridis_colors = clrs.colorscale_to_colors(
              clrs.PLOTLY_SCALES["Viridis"]
          )
          viridis_colors = clrs.color_parser(
              viridis_colors, clrs.hex_to_rgb
          )
          viridis_colors = clrs.color_parser(
              viridis_colors, clrs.label_rgb
          )
          viri_len = len(viridis_colors) + 1
          viri_intervals = utils.endpts_to_intervals(
              list(np.linspace(0, 1, viri_len))
          )[1:-1]
  
          for L in np.linspace(0, 1, len(LEVELS)):
              for idx, inter in enumerate(viri_intervals):
                  if L == 0:
                      break
                  elif inter[0] < L <= inter[1]:
                      break
  
              intermed = (L - viri_intervals[idx][0]) / (
                  viri_intervals[idx][1] - viri_intervals[idx][0]
              )
  
              float_color = clrs.find_intermediate_color(
                  viridis_colors[idx],
                  viridis_colors[idx],
                  intermed,
                  colortype="rgb",
              )
  
              # make R,G,B into int values
              float_color = clrs.unlabel_rgb(float_color)
              float_color = clrs.unconvert_from_RGB_255(float_color)
              int_rgb = clrs.convert_to_RGB_255(float_color)
              int_rgb = clrs.label_rgb(int_rgb)
  
              colorscale.append(int_rgb)
  
      if len(colorscale) < len(LEVELS):
          raise PlotlyError(
              "You have {} LEVELS. Your number of colors in 'colorscale' must "
              "be at least the number of LEVELS: {}. If you are "
              "using 'binning_endpoints' then 'colorscale' must have at "
              "least len(binning_endpoints) + 2 colors".format(
                  len(LEVELS), min(LEVELS, LEVELS[:20])
              )
          )
  
      color_lookup = dict(zip(LEVELS, colorscale))
      x_traces = dict(zip(LEVELS, [[] for i in range(len(LEVELS))]))
      y_traces = dict(zip(LEVELS, [[] for i in range(len(LEVELS))]))
  
      # scope
      if isinstance(scope, str):
          raise PlotlyError("'scope' must be a list/tuple/sequence")
  
      scope_names = []
      extra_states = [
          "Alaska",
          "Commonwealth of the Northern Mariana Islands",
          "Puerto Rico",
          "Guam",
          "United States Virgin Islands",
          "American Samoa",
      ]
      for state in scope:
          if state.lower() == "usa":
              scope_names = df["STATE_NAME"].unique()
              scope_names = list(scope_names)
              for ex_st in extra_states:
                  try:
                      scope_names.remove(ex_st)
                  except ValueError:
                      pass
          else:
              if state in st_to_state_name_dict.keys():
                  state = st_to_state_name_dict[state]
              scope_names.append(state)
      df_state = df_state[df_state["STATE_NAME"].isin(scope_names)]
  
      plot_data = []
      x_centroids = []
      y_centroids = []
      centroid_text = []
      fips_not_in_shapefile = []
      if not binning_endpoints:
          for index, f in enumerate(fips):
              level = values[index]
              try:
                  fips_polygon_map[f].type
  
                  (
                      x_traces,
                      y_traces,
                      x_centroids,
                      y_centroids,
                      centroid_text,
                  ) = _calculations(
                      df,
                      fips,
                      values,
                      index,
                      f,
                      simplify_county,
                      level,
                      x_centroids,
                      y_centroids,
                      centroid_text,
                      x_traces,
                      y_traces,
                      fips_polygon_map,
                  )
              except KeyError:
                  fips_not_in_shapefile.append(f)
  
      else:
          for index, f in enumerate(fips):
              for j, inter in enumerate(intervals):
                  if inter[0] < values[index] <= inter[1]:
                      break
              level = LEVELS[j]
  
              try:
                  fips_polygon_map[f].type
  
                  (
                      x_traces,
                      y_traces,
                      x_centroids,
                      y_centroids,
                      centroid_text,
                  ) = _calculations(
                      df,
                      fips,
                      values,
                      index,
                      f,
                      simplify_county,
                      level,
                      x_centroids,
                      y_centroids,
                      centroid_text,
                      x_traces,
                      y_traces,
                      fips_polygon_map,
                  )
              except KeyError:
                  fips_not_in_shapefile.append(f)
  
      if len(fips_not_in_shapefile) > 0:
          msg = (
              "Unrecognized FIPS Values\n\nWhoops! It looks like you are "
              "trying to pass at least one FIPS value that is not in "
              "our shapefile of FIPS and data for the counties. Your "
              "choropleth will still show up but these counties cannot "
              "be shown.\nUnrecognized FIPS are: {}".format(
                  fips_not_in_shapefile
              )
          )
          warnings.warn(msg)
  
      x_states = []
      y_states = []
      for index, row in df_state.iterrows():
          if df_state["geometry"][index].type == "Polygon":
              x = (
                  row.geometry.simplify(simplify_state)
                  .exterior.xy[0]
                  .tolist()
              )
              y = (
                  row.geometry.simplify(simplify_state)
                  .exterior.xy[1]
                  .tolist()
              )
              x_states = x_states + x
              y_states = y_states + y
          elif df_state["geometry"][index].type == "MultiPolygon":
              x = [
                  poly.simplify(simplify_state).exterior.xy[0].tolist()
                  for poly in df_state["geometry"][index]
              ]
              y = [
                  poly.simplify(simplify_state).exterior.xy[1].tolist()
                  for poly in df_state["geometry"][index]
              ]
              for segment in range(len(x)):
                  x_states = x_states + x[segment]
                  y_states = y_states + y[segment]
                  x_states.append(np.nan)
                  y_states.append(np.nan)
          x_states.append(np.nan)
          y_states.append(np.nan)
  
      for lev in LEVELS:
          county_data = dict(
              type="scatter",
              mode="lines",
              x=x_traces[lev],
              y=y_traces[lev],
              line=county_outline,
              fill="toself",
              fillcolor=color_lookup[lev],
              name=lev,
              hoverinfo="none",
          )
          plot_data.append(county_data)
  
      if show_hover:
          hover_points = dict(
              type="scatter",
              showlegend=False,
              legendgroup="centroids",
              x=x_centroids,
              y=y_centroids,
              text=centroid_text,
              name="US Counties",
              mode="markers",
              marker={"color": "white", "opacity": 0},
              hoverinfo="text",
          )
          centroids_on_select = dict(
              selected=dict(marker=centroid_marker),
              unselected=dict(marker=dict(opacity=0)),
          )
          hover_points.update(centroids_on_select)
          plot_data.append(hover_points)
  
      if show_state_data:
          state_data = dict(
              type="scatter",
              legendgroup="States",
              line=state_outline,
              x=x_states,
              y=y_states,
              hoverinfo="text",
              showlegend=False,
              mode="lines",
          )
          plot_data.append(state_data)
  
      DEFAULT_LAYOUT = dict(
          hovermode="closest",
          xaxis=dict(
              autorange=False,
              range=USA_XRANGE,
              showgrid=False,
              zeroline=False,
              fixedrange=True,
              showticklabels=False,
          ),
          yaxis=dict(
              autorange=False,
              range=USA_YRANGE,
              showgrid=False,
              zeroline=False,
              fixedrange=True,
              showticklabels=False,
          ),
          margin=dict(t=40, b=20, r=20, l=20),
          width=900,
          height=450,
          dragmode="select",
          legend=dict(
              traceorder="reversed",
              xanchor="right",
              yanchor="top",
              x=1,
              y=1,
          ),
          annotations=[],
      )
      fig = dict(data=plot_data, layout=DEFAULT_LAYOUT)
      fig["layout"].update(layout_options)
      fig["layout"]["annotations"].append(
          dict(
              x=1,
              y=1.05,
              xref="paper",
              yref="paper",
              xanchor="right",
              showarrow=False,
              text="<b>" + legend_title + "</b>",
          )
      )
  
      if len(scope) == 1 and scope[0].lower() == "usa":
          xaxis_range_low = -125.0
          xaxis_range_high = -55.0
          yaxis_range_low = 25.0
          yaxis_range_high = 49.0
      else:
          xaxis_range_low = float("inf")
          xaxis_range_high = float("-inf")
          yaxis_range_low = float("inf")
          yaxis_range_high = float("-inf")
          for trace in fig["data"]:
              if all(isinstance(n, Number) for n in trace["x"]):
                  calc_x_min = min(trace["x"] or [float("inf")])
                  calc_x_max = max(trace["x"] or [float("-inf")])
                  if calc_x_min < xaxis_range_low:
                      xaxis_range_low = calc_x_min
                  if calc_x_max > xaxis_range_high:
                      xaxis_range_high = calc_x_max
              if all(isinstance(n, Number) for n in trace["y"]):
                  calc_y_min = min(trace["y"] or [float("inf")])
                  calc_y_max = max(trace["y"] or [float("-inf")])
                  if calc_y_min < yaxis_range_low:
                      yaxis_range_low = calc_y_min
                  if calc_y_max > yaxis_range_high:
                      yaxis_range_high = calc_y_max
  
      # camera zoom
      fig["layout"]["xaxis"]["range"] = [
          xaxis_range_low,
          xaxis_range_high,
      ]
      fig["layout"]["yaxis"]["range"] = [
          yaxis_range_low,
          yaxis_range_high,
      ]
  
      # aspect ratio
      if asp is None:
          usa_x_range = USA_XRANGE[1] - USA_XRANGE[0]
          usa_y_range = USA_YRANGE[1] - USA_YRANGE[0]
          asp = usa_x_range / usa_y_range
  
      # based on your figure
      width = float(
          fig["layout"]["xaxis"]["range"][1]
          - fig["layout"]["xaxis"]["range"][0]
      )
      height = float(
          fig["layout"]["yaxis"]["range"][1]
          - fig["layout"]["yaxis"]["range"][0]
      )
  
      center = (
          sum(fig["layout"]["xaxis"]["range"]) / 2.0,
          sum(fig["layout"]["yaxis"]["range"]) / 2.0,
      )
  
      if height / width > (1 / asp):
          new_width = asp * height
          fig["layout"]["xaxis"]["range"][0] = (
              center[0] - new_width * 0.5
          )
          fig["layout"]["xaxis"]["range"][1] = (
              center[0] + new_width * 0.5
          )
      else:
          new_height = (1 / asp) * width
          fig["layout"]["yaxis"]["range"][0] = (
              center[1] - new_height * 0.5
          )
          fig["layout"]["yaxis"]["range"][1] = (
              center[1] + new_height * 0.5
          )
  
      return go.Figure(fig)
  
file_path: catboost/contrib/python/plotly/py3/plotly/figure_factory/_county_choropleth.py
imports: import io
  import numpy
  import os
  import pandas
  import warnings
  from math import log, floor
  from numbers import Number
  from plotly import optional_imports
  import plotly.colors
  from plotly.figure_factory import utils
  from plotly.exceptions import PlotlyError
  import plotly.graph_objs
label_desc: Use image node_id 5 for calling a global function with example usage: create_choropleth(fips, values, scope, binning_endpoints, colorscale, order, simplify_county, simplify_state, asp, show_hover, show_state_data, state_outline, county_outline, centroid_marker, round_legend_values, exponent_format, legend_title) and returns: go