# mapclassify.greedy

mapclassify.greedy(gdf, strategy='balanced', balance='count', min_colors=4, sw='queen', min_distance=None, silence_warnings=True, interchange=False)

  Color GeoDataFrame using various strategies of greedy (topological) colouring.

  Attempts to color a GeoDataFrame using as few colors as possible, where no neighbours can have same color as the feature itself. Offers various strategies ported from QGIS or implemented within NetworkX for greedy graph coloring.

  `greedy` will return `pandas.Series` representing assigned color codes.

  Parameters:
    gdf `GeoDataFrame`

      GeoDataFrame

    strategy `str` (`default` ‘balanced’)

      Determine coloring strategy. Options are `'balanced'` for algorithm based on QGIS Topological coloring. It is aiming for a visual balance, defined by the balance parameter. Other options are those supported by `networkx.greedy_color`:

      - `'largest_first'`

      - `'random_sequential'`

      - `'smallest_last'`

      - `'independent_set'`

      - `'connected_sequential_bfs'`

      - `'connected_sequential_dfs'`

      - `'connected_sequential'` (alias for the previous strategy)

      - `'saturation_largest_first'`

      - `'DSATUR'` (alias for the previous strategy)

      For details see https://networkx.github.io/documentation/stable/reference/algorithms/generated/networkx.algorithms.coloring.greedy_color.html

    balance `str` (`default` ‘count’)

      If strategy is `'balanced'`, determine the method of color balancing.

      - `'count'` attempts to balance the number of features per each color.

      - `'area'` attempts to balance the area covered by each color.

      - `'centroid'` attempts to balance the distance between colors based on the distance between centroids.

      - `'distance'` attempts to balance the distance between colors based on the distance between geometries. Slower than `'centroid'`, but more precise.

      Both `'centroid'` and `'distance'` are significantly slower than other especially for larger GeoDataFrames. Apart from `'count'`, all require CRS to be projected (not in degrees) to ensure metric values are correct.

    min_colors: int (default 4)

      If strategy is `'balanced'`, define the minimal number of colors to be used.

    sw‘queen’, ‘rook’ or `libpysal.weights.W` (`default` ‘queen’)

      If min_distance is None, one can pass `'libpysal.weights.W'` object denoting neighbors or let greedy generate one based on `'queen'` or `'rook'` contiguity.

    min_distance `float` (`default` `None`)

      Set minimal distance between colors. If `min_distance` is not `None`, slower algorithm for generating spatial weghts is used based on intersection between geometries. `'min_distance'` is then used as a tolerance of intersection.

    silence_warnings bool (`default` `True`)

      Silence libpysal warnings when creating spatial weights.

    interchange bool (`default` `False`)

      Use the color interchange algorithm (applicable for NetworkX strategies). For details see https://networkx.github.io/documentation/stable/reference/algorithms/generated/networkx.algorithms.coloring.greedy_color.html

  Returns:
    color `pandas.Series`

      `pandas.Series` representing assinged color codes.

  Examples

      >>> from mapclassify import greedy
      >>> import geopandas
      >>> world = geopandas.read_file(
      ...     "https://naciscdn.org/naturalearth/110m/cultural/ne_110m_admin_0_countries.zip"
      ... )
      >>> africa = world.loc[world.CONTINENT == "Africa"].copy()
      >>> africa = africa.to_crs("ESRI:102022").reset_index(drop=True)

  Default:

      >>> africa["greedy_colors"] = greedy(africa)
      >>> africa["greedy_colors"].head()
      0    1
      1    0
      2    0
      3    1
      4    4
      Name: greedy_colors, dtype: int64

  Balanced by area:

      >>> africa["balanced_area"] = greedy(africa, strategy="balanced", balance="area")
      >>> africa["balanced_area"].head()
      0    1
      1    2
      2    0
      3    1
      4    3
      Name: balanced_area, dtype: int64

  Using rook adjacency:

      >>> africa["rook_adjacency"] = greedy(africa, sw="rook")
      >>> africa["rook_adjacency"].tail()
      46    3
      47    0
      48    2
      49    3
      50    1
      Name: rook_adjacency, dtype: int64

  Adding minimal distance between colors:

      >>> africa["min_distance"] = greedy(africa, min_distance=1000000)
      >>> africa["min_distance"].head()
      0    1
      1    9
      2    0
      3    7
      4    4
      Name: min_distance, dtype: int64

  Using different coloring strategy:

      >>> africa["smallest_last"] = greedy(africa, strategy="smallest_last")
      >>> africa["smallest_last"].head()
      0    3
      1    1
      2    1
      3    3
      4    1
      Name: smallest_last, dtype: int64