How to assign a colour based on shapefile polygon value?

Question

I am plotting a shapefile over a basemap region. To be specific the column variable "PhytoNAOCo" is plotted on the map. There are some rows (polygons) where the value is 0.00, and I would like to assign a white colour to those polygons, to differentiate it from the others. Here's my code used so far:

pip install geopandas
pip install contextily
import geopandas as gpd
import contextily as ctx

data = gpd.read_file("NAOAMOCorrEcoRegion.shp")
import matplotlib.pyplot as plt
ax=data.plot(figsize=(12,10), column="PhytoNAOCo", legend=True, cmap='bwr')
map = Basemap(llcrnrlon=-50,llcrnrlat=30,urcrnrlon=50.,urcrnrlat=80.,
             resolution='i', lat_0 = 39.5, lon_0 = 1)
map.fillcontinents(color='lightgreen')
map.drawcoastlines()
plt.title("Correlation")

Image

And here's the link to the file - https://drive.google.com/file/d/1VbSYP_kKi8IBSQaDTKjiBYb0PIfA9me9/view?usp=sharing

I guess there would be some kind of loop involved in this case (like if data.PhytoNAOCo(i) == 0, else... then maybe assign a colour?) but I'm new to Python, so I'm not sure how to go about it.

Answer 1

• effectively this is covered by geopandas plot missing_kwds
• hence set rows with zero to NaN then use this inbuilt capability

import contextily as ctx
from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt
import numpy as np

# data = gpd.read_file("NAOAMOCorrEcoRegion.shp")
# set 0 to nan
data.loc[data["PhytoNAOCo"].eq(0), "PhytoNAOCo"] = np.nan

ax = data.plot(
    figsize=(12, 10),
    column="PhytoNAOCo",
    legend=True,
    cmap="bwr",
    missing_kwds={"color":"white"},
)
map = Basemap(
    llcrnrlon=-50,
    llcrnrlat=30,
    urcrnrlon=50.0,
    urcrnrlat=80.0,
    resolution="i",
    lat_0=39.5,
    lon_0=1,
)
map.fillcontinents(color="lightgreen")
map.drawcoastlines()
plt.title("Correlation")

source data

from io import BytesIO
from urllib.request import urlopen
from zipfile import ZipFile
from pathlib import Path
import tempfile
import geopandas as gpd

# zipurl = 'https://drive.google.com/file/d/1VbSYP_kKi8IBSQaDTKjiBYb0PIfA9me9/view?usp=sharing'
zipurl = 'https://drive.google.com/uc?export=download&id=1VbSYP_kKi8IBSQaDTKjiBYb0PIfA9me9'

with tempfile.TemporaryDirectory() as d:
    with urlopen(zipurl) as zipresp:
        with ZipFile(BytesIO(zipresp.read())) as zfile:
            zfile.extractall(d)
    data = gpd.read_file(list(Path(d).glob("*.shp"))[0])

alternative using simple world map for continents

import matplotlib.pyplot as plt
import numpy as np

# data = gpd.read_file("NAOAMOCorrEcoRegion.shp")
# set 0 to nan
data.loc[data["PhytoNAOCo"].eq(0), "PhytoNAOCo"] = np.nan

ax = data.plot(
    figsize=(12, 10),
    column="PhytoNAOCo",
    legend=True,
    cmap="bwr",
    missing_kwds={"color": "white"},
)
ax = (
    gpd.read_file(gpd.datasets.get_path("naturalearth_lowres"))
    .dissolve() # just want coastlines, so combine all geometries
    .clip_by_rect(*data.total_bounds) # clip to bounds of interesting geometry...
    .plot(color="lightgreen", edgecolor="black", ax=ax)
)

plt.title("Correlation")