Location of Farmacias Similares in Mexico

A Geographical Overview

Author

Jesus LM

Published

Jan, 2026

Abstract

In this post, we will show the sundry locations of Farmacias Smilares alongside Mexico.

# import libraries
import pandas as pd
import requests
import folium
import folium.plugins

#url = 'https://www.farmaciasdesimilares.com/getpickuppoints'

# request data
#response = requests.get(url)
#stores = response.json()

# get data
#stores[0]

# create columns
#drugstore=[]
#city=[]
#state=[]
#neighborhood=[]
#street=[]
#zip_code=[]
#number=[]
#latitude=[]
#longitude=[]

#for store in range(len(stores)):
#    drugstore.append(stores[store]['name'])
#    city.append(stores[store]['address']['city'])
#    state.append(stores[store]['address']['state'])
#    neighborhood.append(stores[store]['address']['neighborhood'])
#    street.append(stores[store]['address']['street'])
#    number.append(stores[store]['address']['number'])
#    zip_code.append(stores[store]['address']['postalCode'])
#    latitude.append(stores[store]['address']['location']['latitude'])
#    longitude.append(stores[store]['address']['location']['longitude'])

# create dataframe
#simi_df = pd.DataFrame(
#    {'drugstore':drugstore,
#    'street':street,
#    'number':number,
#    'neighborhood':neighborhood,
#    'city': city,
#    'zip_code':zip_code,
#    'latitude': latitude,
#    'longitude': longitude,}
#)

# save csv
#simi_df.to_csv('similares.csv')

simi_df = pd.read_csv('similares.csv')
simi_df.head()

	Unnamed: 0	drugstore	street	number	neighborhood	city	zip_code	latitude	longitude
0	0	ACAPULCO 1	GUERRERO	ED A	PROGRESO	ACAPULCO DE JUAREZ	39350	16.86016	-99.90353
1	1	ACAPULCO 10	RUIZ CORTINEZ	8	ALTA PROGRESO	ACAPULCO DE JUAREZ	39610	16.87317	-99.89115
2	2	ACAPULCO 11	GRAN VIA EL COLOSO	LT 10	LA ESPERANZA	ACAPULCO DE JUAREZ	39610	16.84682	-99.81231
3	3	ACAPULCO 13	LAZARO CARDENAS	36	LAS CRUCES	ACAPULCO DE JUAREZ	39902	16.88586	-99.83698
4	4	ACAPULCO 14	CUAUHTEMOC	129	PROGRESO	ACAPULCO DE JUAREZ	39350	16.85863	-99.89725

simi_face = 'https://tinyurl.com/3mvfbu4t'

# map
simi_map = folium.Map(location=[23, -101], zoom_start=5, attr='Google')

for i in range(0,len(simi_df)):
    # logo marker
    folium.Marker(location=[simi_df['latitude'][i], simi_df['longitude'][i]],
                  popup=folium.Popup(
                      f"<b>Drugstore:</b> {simi_df['drugstore'][i]} \
                      <br><b>Street:</b> {simi_df['street'][i]} \
                      <br><b>Neighborhood:</b> {simi_df['neighborhood'][i]}"
                      ),
                  icon=folium.features.CustomIcon(
                      simi_face,
                      icon_size=(40,40)
                      )
                 ).add_to(simi_map)

folium.plugins.Fullscreen(
    position="topleft",
    title="Expand me",
    title_cancel="Exit me",
    force_separate_button=True,
    ).add_to(simi_map)

<folium.plugins.fullscreen.Fullscreen at 0x7c52f8c63110>

simi_map

Make this Notebook Trusted to load map: File -> Trust Notebook

References

Pacheco, C. (2025) simi_ubicas. Github.

Contact

Jesus LM
Economist & Data Scientist

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