Series plot - Geopandas - matplotlib

Question

I dont have a working code - but a snipet of my code can be as follows. I'm trying to use geopandas with mathplotlib, and trying to plot a map with links and points.
shape_file = os.path.join(os.getcwd(), "Healthboard")
healthboard = gp.read_file(os.path.join(shape_file, "healthboard.shp"))
healthboard = healthboard.to_crs({'init': 'epsg:4326'}) # re-projection
geo_df1 = geo_df1[geo_df1['HealthBoardArea2019Code'] == string1]
geo = geo_df[geo_df['Healthboard '] == string2]
new_shape_file = os.path.join(os.getcwd(), "Council_Shapefile")
council_to_healtboard = pd.read_csv("council_to_healthboard.csv")
council_to_healthboard = council_to_healtboard.rename(columns = {'CA': 'Council_area_code'})
council = gp.read_file(os.path.join(new_shape_file, "Council_shapefile.shp"))
council = council.to_crs({'init': 'epsg:4326'})
council = council.rename(columns = {'la_s_code':'Council_area_code'})
df = council.merge(council_to_healthboard, on = 'Council_area_code', how ='inner')
# Plotting stuff
fig, ax = plt.subplots(figsize=(15,15))
geo_df1.plot(ax = ax, markersize=35, color = "blue", marker = "*", label = "Postcode Sector")
geo.geometry.plot(ax = ax, color = "red", markersize=20, alpha = 0.8, label = 'SiteName')
#healthboard[healthboard["HBName"]=="Lothian"].plot(ax = ax, alpha = 0.6)
#healthboard[healthboard["HBName"]=="Lothian"].boundary.plot(ax = ax, color = "black", alpha = 0.6)
df[df["HB"]=="S08000024"].boundary.plot(ax =ax, color = "black", alpha = 0.1)
df[df["HB"]=="S08000024"].plot(ax =ax, cmap = "viridis", alpha = 0.1)
links_gp.plot(ax =ax, alpha = 0.25, color='brown', linestyle = "-")
My links_gp.plot has 40 time periods, as a result I want to make one plot, and have a button to adjust the parameters of time. Or if not possible a series of 40 plots. I've tried numerous ways but keep failing on this. I would really appreciate if someone could guide me on this.

Answer 1

I'm aware that you are using matplotlib, but if you don't mind using bokeh instead, you could use the following. To create an interactive plot with a possibility to adjust a parameter, bokeh provides a slider widget which can be used to change the plot based on a custom filter function.
An example from a geopandas dataframe with LineString geometries similar to the one you posted:
import geopandas as gpd
from bokeh.io import show, output_notebook
from bokeh.models import (CDSView, ColumnDataSource, CustomJS,
CustomJSFilter, Slider, Column)
from bokeh.layouts import column
from bokeh.plotting import figure
# prepare data source
links_gp['x'] = links_gp.apply(lambda row: list(row['geometry'].coords.xy[0]), axis=1)
links_gp['y'] = links_gp.apply(lambda row: list(row['geometry'].coords.xy[1]), axis=1)
# drop geometry column, because it can't be serialized to ColumnDataSource
links_gp.drop('geometry', axis=1, inplace=True)
linesource = ColumnDataSource(links_gp)
p = figure(title = 'Bokeh Time Slider',
plot_height = 500,
plot_width = 600,
toolbar_location = 'below',
tools = "pan, wheel_zoom, box_zoom, reset")
slider = Slider(title='Time Period', start=1, end=40, step=1, value=1)
# Callback triggers the filter when the slider moves
callback = CustomJS(args=dict(source=linesource),
code="""source.change.emit();""")
slider.js_on_change('value', callback)
# Custom filter that selects all lines of the time period based on the slider value
custom_filter = CustomJSFilter(args=dict(slider=slider),
code="""
var indices = [];
// iterate through rows of data source and check if time period value equals the slider value
for (var i = 0; i < source.get_length(); i++){
if (source.data['Time Period'][i] == slider.value){
indices.push(true);
} else {
indices.push(false);
}
}
return indices;
""")
# Use filter to determine which lines are visible
view = CDSView(source=linesource, filters=[custom_filter])
# plot lines to map
p.multi_line('x', 'y', source=linesource, color='red', line_width=3, view=view)
layout = column(p, slider)
show(layout)
This will be the result of the above code.