geopandas map centering with countries

Question

I am plotting data for ex-USSR on geopandas and it really does not look nice.

I tried this code published early on 1, 2, but adds no help hence I don't need just map, I need to put data on it. And because I'm merging the 'world' (actually consisting only of ex-USSR countries) and coronavirus data "on 'Country'", then I need that dataframe with original country and adjusted polygons.

Key pieces of my code:

url = "https://opendata.arcgis.com/datasets/a21fdb46d23e4ef896f31475217cbb08_1.geojson"
world = gpd.read_file(url)
ex_ussr = ['Ukraine', 'Belarus', 'Kyrgyzstan', 'Azerbaijan', 'Tajikistan', 'Armenia', 'Georgia', 'Russia', 'Kazakhstan', 'Lithuania', 'Latvia', 'Estonia', 'Uzbekistan']
world = world[world['CNTRY_NAME'].isin(ex_ussr)]
df_world = pd.merge(df_covid, world, on='Country')
crs = {'init': 'epsg:4326'}
corona_gpd = gpd.GeoDataFrame(df_world, crs=crs, geometry='geometry')
f, ax = plt.subplots(1, 1, figsize=(30,5))
ax = corona_gpd.plot(column='New cases', cmap='rainbow', ax=ax, legend=True, legend_kwds={'label': 'New Cases by Country'})

Answer 1

This is a challenging question that I am happy to try. The following is a runnable code that will create a geodataframe of russia with good geometry -- the geometry that does not spread apart at the dateline.

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

#import cartopy.crs as ccrs
#import cartopy

from shapely.geometry import LineString, MultiPolygon, Polygon
from shapely.ops import split
from shapely.affinity import translate
import geopandas

def shift_geom(shift, gdataframe, plotQ=False):
    # this code is adapted from somewhere found in SO
    # *** will give credit here ***
    shift -= 180
    moved_map = []
    splitted_map = []
    border = LineString([(shift,90),(shift,-90)])

    for row in gdataframe["geometry"]:
        splitted_map.append(split(row, border))
    for element in splitted_map:
        items = list(element)
        for item in items:
            minx, miny, maxx, maxy = item.bounds
            if minx >= shift:
                moved_map.append(translate(item, xoff=-180-shift))
            else:
                moved_map.append(translate(item, xoff=180-shift))

    # got `moved_map` as the moved geometry            
    gdf = geopandas.GeoDataFrame({"geometry": moved_map})
    # can move back to original pos by rerun with -ve shift

    # can change crs here
    if plotQ:
        fig, ax = plt.subplots()
        gdf.plot(ax=ax)
        plt.show()

    return gdf


world = geopandas.read_file(geopandas.datasets.get_path('naturalearth_lowres'))
world = world[['name', 'continent', 'geometry', 'pop_est', 'gdp_md_est']]

ex_ussr = ['Ukraine', 'Belarus', 'Kyrgyzstan', 'Azerbaijan', 'Tajikistan', 'Armenia', \
           'Georgia', 'Kazakhstan', 'Lithuania', 'Latvia', 'Estonia', 'Uzbekistan']

# ex_ussr w/o russia
ex_ussr_gdf = world[world['name'].isin(ex_ussr)]
# russia only
russia = world[ world['name']=='Russia' ]

# manipulate russia's geometry
rus_shift_90 = shift_geom(90, russia, False)        # Do not plot
good_geom_rus = shift_geom(-90, rus_shift_90, True) # Plot it

# a plot of new geometry appears

# Create geodataframe with 1 row (Multi-polygon) using this geometry
newrus_gdf = geopandas.GeoDataFrame( { "name": ["Russia"] , "new_geometry": [good_geom_rus.geometry.unary_union]}, \
                             geometry="new_geometry", crs="EPSG:4326")
# Merge `russia` with `newrus_gdf` to get everything in 1 dataframe
russia_final = russia.merge(right=newrus_gdf , on="name")

# Set the `new_geometry` from `newrus_gdf` as the geometry
russia_final.set_geometry("new_geometry", drop=True, inplace=True)

# plot all ex_ussr together = `russia_final` + `ex_ussr_gdf`
rus_ax = russia_final.plot(color='brown')
ex_ussr_gdf.plot(ax=rus_ax, color="green", ec="black", lw=0.3, alpha=0.75)

Edit

To add russia_final to ex_ussr_gdf and plot the result, run this code:-

ex_ussr_gdf = ex_ussr_gdf.append(russia_final, ignore_index=True)
ex_ussr_gdf.plot(color="pink", ec="black", lw=0.3)