Plot the bar legend instead of ranges when using quantiles scheme in geopandas plot-CodePudding

Let's say I have the data and I use geopandas plot function to create the following map

import pandas as pd
import geopandas as gpd
import matplotlib.pyplot as plt

world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))
world['gdp_per_cap'] = world.gdp_md_est / world.pop_est
world.plot(column='gdp_per_cap', cmap = 'Blues', scheme='quantiles', k = 10,legend=True)
plt.show()

As you see it generates the legend with the range values. Is it possible to change this legend and plot the standard bar legend with different (sequantial) colours?

I know that the quantiles scheme creates the classes and you can't treat them as a continuous variable, but somehow it should still be possible to construct a bar legend similar to one when I delete scheme='quantiles', k = 10 from the code above.

Thanks for your help

CodePudding user response：

When qualitative colormap is needed for a thematic plot, one can find it

If sequential colormap is used instead of qualitative colormap, the plot can be created similarly. Here I use Blues colormap. This is more appropriate for the GDP per capita data.

EDIT My apology for some errors on the code/map:- GPD ---> GDP

The legend's label texts can be accessed and edited as needed. Additional code for such operation follows:-

# Manipulate the legend's label texts
# Replace range_text with mid_range as new label texts
for eb in leg1.get_texts():
    txt = eb.get_text()
    low,high = [float(tt) for tt in  txt.split(sep=",")]
    avg = (low high)/2        # compute mid-range values
    #print(low,high,avg)
    eb.set_text(f"{avg:.2f}") # midrange values, 2 deci digits
    #eb.set_text(f"")         # blank-out text

The changes will be:

Old text         --> New text
  0.570,   1.655 --> 1.11
  1.655,   3.053 --> 2.35
  3.053,   4.813 --> 3.93
  4.813,   8.357 --> 6.58
  8.357,  11.620 --> 9.99
 11.620,  15.776 --> 13.70
 15.776,  24.574 --> 20.18
 24.574,  35.761 --> 30.17
 35.761,  48.513 --> 42.14
 48.513, 200.000 --> 124.26