Develop Reference

How to plot addresses (Lat/Long) from a csv on JSON map.?

So I am trying to do something which seems relatively simple but is proving incredibly difficult. I have a .csv file with addresses and their correspondent latitude/longitude, I just want to plot those on a California JSON map like this one in python:
https://github.com/deldersveld/topojson/blob/master/countries/us-states/CA-06-california-counties.json
I've tried bubble maps, scatter maps, etc. but to no luck I keep getting all kind of errors :(. This is the closest I've got, but that uses a world map and can't zoom in effectively like that json map up there. I am still learning python so please go easy on me ><
import plotly.express as px
import pandas as pd
df = pd.read_csv(r"C:\Users\FT4\Desktop\FT Imported Data\Calimapdata.csv")
fig = px.scatter_geo(df,lat='Latitude',lon='Longitude', hover_name="lic_type", scope="usa")
fig.update_layout(title = 'World map', title_x=0.5)
fig.show()
If anyone could help me with this I would appreciate it. Thank you

your example link is just a GeoJSON geometry definition. Are you talking about a Choropleth?
If so, check out geopandas - you should be able to link your data to the polygons in the shape definition you linked to by reading it in with geojson and then joining on the shapes with sjoin. Once you have data tied to each geometry, you can plot with geopandas's .plot method. Check out the user guide for more info.
Something along these lines should work:
import geopandas as gpd, pandas as pd
geojson_raw_url = (
"https://raw.githubusercontent.com/deldersveld/topojson/"
"master/countries/us-states/CA-06-california-counties.json"
)
gdf = gpd.read_file(geojson_raw_url, engine="GeoJSON")
df = pd.read_csv(r"C:\Users\FT4\Desktop\FT Imported Data\Calimapdata.csv")
merged = gpd.sjoin(gdf, df, how='right')
# you could plot this directly with geopandas
merged.plot("lic_type")
alternatively, using #r-beginners' excellent answer to another question, we can plot with express:
fig = px.choropleth(merged, geojson=merged.geometry, locations=merged.index, color="lic_type")
fig.update_geos(fitbounds="locations", visible=False)
fig.show()

Datashader on Google Colab - blank output

I'm trying out Datashader on Google Colab to visualise a large dataset of longitudes and latitudes colored logarithmically with the colorcet.fire colormaps, but my code throws a completely blank output.
Code in text:
import datashader as ds
import pandas as pd
import colorcet
data = pd.read_csv('hab.csv', usecols=['longitude','latitude'])
cvs = ds.Canvas()
agg = cvs.points(data, 'latitude', 'longitude')
ds.tf.set_background(ds.tf.shade(agg, cmap=colorcet.fire, how='log'))
What I see on Colab:

I'm not a collab user, but yes, when I run your code locally with the five datapoints shown I get a blank plot. In my local version, it's because the code is specifying a colormap whose highest value is white, and for a few scattered points each of them are at the highest value. The code uses set_background, perhaps trying to set the background to black as would be suitable for that colormap, but it doesn't specify "black" and so the set_background call does nothing. If I specify the background color and add Datashader spreading so that these single datapoints are easier to see, I do get a plot from your code:
cvs = ds.Canvas()
agg = cvs.points(data, 'latitude', 'longitude')
ds.tf.set_background(ds.tf.shade(ds.tf.spread(agg, px=10), cmap=colorcet.fire, how='log'), "black")
You may have some other problem as well, though, since the plot you showed wasn't just white, it appeared to be transparent. And if your dataset is indeed large, you should see output anyway, because data points would then overlap and use all the colors in the colormap.

Python: Masking ERA5 data (NetCDF) from shapefile (polygon/multipolygon)

I want to select grid cells from ERA5 gridded data (surface level only) that are inside geographical masks for North- and South-Switzerland (plus the radar buffer), to calculate regional means.
The 4 masks (masks) are given as polygons/multipolygons (polygons) in a shapefile and so far for 2 of the masks I was able to use salem roi to get what I want:
radar_north = salem.read_shapefile('radar_north140.shp')
file_radar_north = file.salem.roi(shape=radar_north)
file_radar_north.cape.mean(dim='time').salem.quick_map()
However, for the radar_south and alpensuedseite shapefiles the code didn´t work at the beginning (wrong selection or shows no data), and now the nothing works anymore (?). I don´t know why, as I have not changed anything from the first time to the second.
If someone sees the issue or knows a different way to mask the ERA data (which is maybe quicker) I would be grateful! (I was unsuccessfull with the answers from similar questions here).
Best
Lena

This could work if you are working on netcdf files
import geopandas as gpd
import xarray as xr
import rioxarray
from shapely.geometry import mapping
# load shapefile with geopandas
radar_north = gpd.read_file('radar_north140.shp')
# load ERA5 netcdf with xarray
era = xr.open_dataset('ERA5.nc')
# add projection system to nc
era = era.rio.write_crs("EPSG:4326", inplace=True)
# mask ERA5 data with shapefile
era_radar_north = era.rio.clip(radar_north.geometry.apply(mapping), radar_north.crs)

Plotly in Python: show mean and variance of selected data

I am generating histograms using go.Histogram as described here. I am getting what is expected:
What I want to do is to show some statistics of the selected data, as shown in the next image (the white box I added manually in Paint):
I have tried this and within the function selection_fn I placed the add_annotation described here. However, it does nothing. No errors too.
How can I do this?
Edit: I am using this code taken from this link
import plotly.graph_objects as go
import numpy as np
x = np.random.randn(500)
fig = go.Figure(data=[go.Histogram(x=x)])
fig.show()
with obviously another data set.

Python plotly choropleth does not work with geoJSONs

I am trying to use plotly choropleth to draw the map, lets say for a random variable of num for each of the feature regions of the map in Italy. However, it does not work. below is the code that I use:
I have downloaded the GeoJson files for Italy from here.
import random
import pandas as pd
import plotly.express as px
import plotly.io as pio
import json
pio.renderers.default='browser'
with open('it-all.geo.json') as f:
geojson = json.load(f)
n_provinces = len(geojson['features'])
province_names = [geojson['features'][k]['properties']['name'] for k in range(n_provinces)]
randomlist = []
for i in range(0,110):
n = random.randint(1,30)
randomlist.append(n)
datadata = pd.DataFrame({'province':province_names, 'num':randomlist})
fig = px.choropleth(datadata, geojson=geojson, color="num",
locations="province", featureidkey="properties.name",
color_continuous_scale="Viridis")
fig.show()
What I am getting is a mixed shape map as below, can anyone please let me know what I am doing wrong, thanks!!

I tried doing the same thing with data from my country and had the same issues. I think that this data might not be readable by plotly. If you look at the website's demos for their maps, there are several javascript scripts running in order to create the maps. It's possible that they've put their geojson into a custom format so that you have to use their javascript services in order to create a comprehensible map.
I later found a different set of data, and was able to easily create a chorpleth map with plotly using the exact same code that didn't work with the original data. Hopefully you found a different dataset that you could use. Oftentimes governments will provide open data about census areas, province/state borders, etc.