set_xlim() does not work with text labels

Question

I am trying to zoom in on geopandas map with labels using set_xlim() in with matplotlib. I basically adapted this SO question to add labels to a map.

However, set_xlim() does not seem to work and did not zoom in on the given extent. (By the way, I've also tried to use text() instead of annotate(), to no avail.)

What I did was the following:

I used the same US county data as in the question linked above, extracted the files, and then executed the following in Jupyter notebook:

import geopandas as gpd
import matplotlib.pyplot as plt
%matplotlib inline

shpfile='shp/cb_2015_us_county_20m.shp'
gdf=gpd.read_file(shpfile)
gdf.plot()

, which gives a map of all US counties as expected:

Adding labels as with one of the answers also works:

ax = gdf.plot()
gdf.apply(lambda x: ax.annotate(s=x.NAME, xy=x.geometry.centroid.coords[0], ha='center'),axis=1);

However, when trying to zoom in to a particular geographic extent with set_xlim() and set_ylim() as follows:

ax = gdf.plot()
gdf.apply(lambda x: ax.annotate(s=x.NAME, xy=x.geometry.centroid.coords[0], ha='center'),axis=1);
ax.set_xlim(-84.2, -83.4)
ax.set_ylim(42, 42.55)

, the two functions do not seem to work. Instead of zooming in, they just trimmed everything outside of the given extent.

If the labeling code is dropped out (gdf.apply(lambda x: ax.annotate(s=x.NAME, xy=x.geometry.centroid.coords[0], ha='center'),axis=1);, the set_xlim() works as expected:

My question is:

What is the correct way to zoom in to an area when labels are present in a plot?

Answer 1

You need some coordinate transformation.

import cartopy.crs as ccrs
# relevant code follows

# set numbers in degrees of longitude
ax.set_xlim(-84.2, -83.4, ccrs.PlateCarree())

# set numbers in degrees of latitude
ax.set_ylim(42, 42.55, ccrs.PlateCarree())
plt.show()

with the option ccrs.PlateCarree(), the input values are transformed to proper data coordinates.

Answer 2

When I try it, I can't draw on matplotlib with the axes restricted. So it's possible to extract the data.

import geopandas as gpd
import matplotlib.pyplot as plt
%matplotlib inline
fig,ax = plt.subplots(1,1, figsize=(4,4), dpi=144)

shpfile = './cb_2015_us_county_20m/cb_2015_us_county_20m.shp'

gdf = gpd.read_file(shpfile)
# gdf = gdf.loc[gdf['STATEFP'] == '27']
gdf['coords'] = gdf['geometry'].apply(lambda x: x.representative_point().coords[:])
gdf['coords'] = [coords[0] for coords in gdf['coords']]
gdf = (gdf[(gdf['coords'].str[0] >= -84.2) & (gdf['coords'].str[0] <= -83.4)
          & (gdf['coords'].str[1] >= 42) & (gdf['coords'].str[1] <= 42.55)])

gdf.plot(ax=ax)
gdf.apply(lambda x: ax.annotate(text=x.NAME, xy=x.geometry.centroid.coords[0], ha='center'),axis=1)