How to display a dynamic map in Power BI using Python script

[powerbi2srm]

Hi!
I'm trying to display a dynamic map in Power BI through Python script editor. This is my code in Python (it works properly)

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

# Read geojson
mapa = gpd.read_file('spain_map.json')
# Read xlsx
pedidos_venta = pd.read_excel("sale_orders.xlsx")
# Merge by the common column
mapa_pedidos_venta = mapa.merge(pedidos_venta, left_on='acom_name', right_on='acon_name_')

# Plot map 
fig, ax = plt.subplots(figsize=(10, 6))
mapa_pedidos_venta.plot(ax=ax)
plt.show()

As I'm looking for getting a dynamic map in Power BI, I've changed this code for Python script editor of Power BI using fields of my Power BI dataset. This dataset has been imported merging the two files mentioned above. I want to use three fields:

• acom_name: names of Spain regions.
• pedidos_venta: number of sale order by region
• geometry: coordenates of regions (imported of geojson file).

Power BI automatically generates the next dataframe in Python script editor:

dataset = pandas.DataFrame(geometry, pedidos_venta, acom_name)

And then this is that I've tried:

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

fig, ax = plt.subplots(figsize=(10, 6))
dataset.plot(ax=ax)
plt.show()

However, I didn't get a map visualization. This code now show me a line graph. Can someone help me, please? Thank you so much!!

[powerbi2srm · Accepted solution]

Firstly, it's neccesary to drag your variables onto the map (in this case, 'acon_name' and 'pedidos_venta'). Then this code will work properly:

import geopandas as gpd
import matplotlib.pyplot as plt

# Reading geojson
mapa = gpd.read_file('spain_map.json')

# Merging by the common column
mapa_pedidos_venta = mapa.merge(dataset, left_on='acom_name', right_on=dataset.acom_name)

# Drawing the map
mapa_pedidos_venta.plot(edgecolor='black', linewidth=1)

# Displaying the map
plt.show()

 It's possible to format your map with something like:

# Defining colors:
def color_range(row):
    if row['pedidos_venta'] > 3000:
        return '#00ff00'
    elif round(row['pedidos_venta'],4) >= 2000:
        return '#FFFF00'
    else:
        return '#FF0000'

# Asigning colors:
color_map = mapa_pedidos_venta.apply(asignar_color,axis=1)

#Changing figsize and dpi
plt.rcParams["figure.figsize"] = (50,25)
plt.rcParams['figure.dpi'] = 140

# Plotting the map
mapa_pedidos_venta.plot(color=color_map, edgecolor='black', linewidth=1)

#Adding labels
for idx, row in mapa_pedidos_venta.iterrows():
    etiqueta = '{:.2%}'.format(row['pedidos_venta'])
    plt.annotate(text=etiqueta, xy=row['geometry'].centroid.coords[0], ha='center', fontsize=40)

# Removing axes
plt.axis('off')

# Displaying the map
plt.show()