How can I use matplotlib.pyplot to customize geopandas plots?-CodePudding

What is the difference between geopandas plots and matplotlib plots? Why are not all keywords available?
In geopandas there is markersize, but not markeredgecolor...
In the example below I plot a pandas df with some styling, then transform the pandas df to a geopandas df. Simple plotting is working, but no additional styling.
This is just an example. In my geopandas plots I would like to customize, markers, legends, etc. How can I access the relevant matplotlib objects?

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

X = np.linspace(-6, 6, 1024)
Y = np.sinc(X)
df = pd.DataFrame(Y, X)
plt.plot(X,Y,linewidth = 3., color = 'k', markersize = 9, markeredgewidth = 1.5, markerfacecolor = '.75', markeredgecolor = 'k', marker = 'o', markevery = 32)
# alternatively:
# df.plot(linewidth = 3., color = 'k', markersize = 9, markeredgewidth = 1.5, markerfacecolor = '.75', markeredgecolor = 'k', marker = 'o', markevery = 32)
plt.show()

# create GeoDataFrame from df
df.reset_index(inplace=True)
df.rename(columns={'index': 'Y', 0: 'X'}, inplace=True)
gdf = gpd.GeoDataFrame(df, geometry=gpd.points_from_xy(df['Y'], df['X']))
gdf.plot(linewidth = 3., color = 'k', markersize = 9) # working
gdf.plot(linewidth = 3., color = 'k', markersize = 9, markeredgecolor = 'k') # not working
plt.show()

CodePudding user response：

You're probably confused by the fact that both libraries named the method .plot(. In matplotlib that specifically translates to a mpl.lines.Line2D object, which also contains the markers and their styling.

Geopandas, assumes you want to plot geographic data, and uses a Path for this (mpl.collections.PathCollection). That has for example the face and edgecolors, but no markers. The facecolor comes into play whenever your path closes and forms a polygon (your example doesn't, making it "just" a line).

Geopandas seems to use a bit of a trick for points/markers, it appears to draw a "path" using the "CURVE4" code (cubic Bézier).

You can explore what's happening if you capture the axes that geopandas returns:

ax = gdf.plot(...

Using ax.get_children() you'll get all artists that have been added to the axes, since this is a simple plot, it's easy to see that the PathCollection is the actual data. The other artists are drawing the axis/spines etc.

[<matplotlib.collections.PathCollection at 0x1c05d5879d0>,
 <matplotlib.spines.Spine at 0x1c05d43c5b0>,
 <matplotlib.spines.Spine at 0x1c05d43c4f0>,
 <matplotlib.spines.Spine at 0x1c05d43c9d0>,
 <matplotlib.spines.Spine at 0x1c05d43f1c0>,
 <matplotlib.axis.XAxis at 0x1c05d036590>,
 <matplotlib.axis.YAxis at 0x1c05d43ea10>,
 Text(0.5, 1.0, ''),
 Text(0.0, 1.0, ''),
 Text(1.0, 1.0, ''),
 <matplotlib.patches.Rectangle at 0x1c05d351b10>]

If you reduce the amount of points a lot, like use 5 instead of 1024, retrieving the Path's drawn show the coordinates and also the codes used:

pcoll = ax.get_children()[0] # the first artist is the PathCollection
path = pcoll.get_paths()[0] # it only contains 1 Path
print(path.codes) # show the codes used.

# array([ 1,  4,  4,  4,  4,  4,  4,  4,  4,  4,  4,  4,  4,  4,  4,  4,  4,
#        4,  4,  4,  4,  4,  4,  4,  4, 79], dtype=uint8)

Some more info about how these paths work can be found at:
https://matplotlib.org/stable/tutorials/advanced/path_tutorial.html

So long story short, you do have all the same keywords as when using Matplotlib, but they're the keywords for Path's and not the Line2D object that you might expect.

You can always flip the order around, and start with a Matplotlib figure/axes created by you, and pass that axes to Geopandas when you want to plot something. That might make it easier or more intuitive when you (also) want to plot other things in the same axes. It does require perhaps a bit more discipline to make sure the (spatial)coordinates etc match.

I personally almost always do that, because it allows to do most of the plotting using the same Matplotlib API's. Which admittedly has perhaps a slightly steeper learning curve. But overall I find it easier compared to having to deal with every package's slightly different interpretation that uses Matplotlib under the hood (eg geopandas, seaborn, xarray etc). But that really depends on where you're coming from.

CodePudding user response：

Thank you for your detailed answer. Based on this I came up with this simplified code from my real project.
I have a shapefile shp and some point data df which I want to plot. shp is plotted with geopandas, df with matplotlib.plt. No need for transferring the point data into a geodataframe gdf as I did initially.

# read marker data (places with coordindates)
df = pd.read_csv("../obese_pct_by_place.csv")
gdf = gpd.GeoDataFrame(df, geometry=gpd.points_from_xy(df['sweref99_lng'], df['sweref99_lat']))
# read shapefile
shp = gpd.read_file("../../SWEREF_Shapefiles/KommunSweref99TM/Kommun_Sweref99TM_region.shp")

fig, ax = plt.subplots(figsize=(10, 8))
ax.set_aspect('equal')
shp.plot(ax=ax)
# plot obesity markers
# geopandas, no edgecolor here
# gdf.plot(ax=ax, marker='o', c='r', markersize=gdf['obese'] * 25)
# matplotlib.pyplot with edgecolor
plt.scatter(df['sweref99_lng'], df['sweref99_lat'], c='r', edgecolor='k', s=df['obese'] * 25)
plt.show()