What will we cover?
- How to plot locations of tweets on a leaflet map using Python
- Setup your access to the Twitter API
- How to collect location data from Twitter and tweets.
- Finally, how to plot it on an interactive leaflet map.
Step 1: Getting ready to collect data from Twitter
Twitter is an amazing place to explore data as the API is easy to get access to and the data is public available to everyone. This is also the case if you want to plot Tweet Locations on a Leaflet Map using Python.
Using Python to interact with Twitter is easy and does require a lot to get started. I prefer to use the tweepy library, which is, as they say, “an easy-to-use Python library to accessing the Twitter API”.
To install the tweepy library, simply type the following in a command shell.
pip install tweepy
The next step is to gather your key values to access the API.
You can get them from https://developer.twitter.com/.
If you need help to get them, I can suggest you follow this tutorial first, which will help you set everything up correctly.
Step 2: Collect the locations from the Tweets
Exploring the data available on a tweet, it has a coordinates and place field.
If you read the first word then you realize.
- Coordinates: Nullable. Represent the geographic location of this Tweet as reported by the user or client application.
- Place: Nullable. When present, indicates that the tweet is associated (but not necessarily originating from) a Place.
Nullable, which mean that it can be null, i.e., have no value.
But let us see how often they are set.
import tweepy def get_twitter_api(): # personal details consumer_key = "___INSERT_YOUR_VALUE_HERE___" consumer_secret = "___INSERT_YOUR_VALUE_HERE___" access_token = "___INSERT_YOUR_VALUE_HERE___" access_token_secret = "___INSERT_YOUR_VALUE_HERE___" # authentication of consumer key and secret auth = tweepy.OAuthHandler(consumer_key, consumer_secret) # authentication of access token and secret auth.set_access_token(access_token, access_token_secret) api = tweepy.API(auth, wait_on_rate_limit=True, wait_on_rate_limit_notify=True) return api def get_twitter_location(search): api = get_twitter_api() count = 0 for tweet in tweepy.Cursor(api.search, q=search).items(500): if hasattr(tweet, 'coordinates') and tweet.coordinates is not None: count += 1 print("Coordinates", tweet.coordinates) if hasattr(tweet, 'location') and tweet.location is not None: count += 1 print("Coordinates", tweet.location) print(count) get_twitter_location("#100DaysOfCode")
Which resulted in 0. I would not expect this to be the case, but you never know.
Hence, the second best thing you can use, is then the location of the user. Most users have a location given in the user object you see the following.
This results in the following way to collect it. We need to check for the object being None.
def get_tweets(search): api = get_twitter_api() location_data =  for tweet in tweepy.Cursor(api.search, q=search).items(500): if hasattr(tweet, 'user') and hasattr(tweet.user, 'screen_name') and hasattr(tweet.user, 'location'): if tweet.user.location: location_data.append((tweet.user.screen_name, tweet.user.location)) return location_data
Here we collect all the locations of the users of the tweets and return a list of them.
Step 3: Plot the data on an interactive map
The folium library is amazing to plot data on an interactive leaflet map.
To install the folium library simply type the following command in a terminal.
pip install folium
Or read more here, on how to install it.
We also need to find the coordinates from each location. This can be done by using the library geopy. It can be installed by typing the following command in a terminal.
pip install geopy
Given that the plotting is done by the following lines of code. Please notice, I put a try-except around the geocode call, as it tends to get an timeout.
import folium from geopy.exc import GeocoderTimedOut from geopy.geocoders import Nominatim def put_markers(map, data): geo_locator = Nominatim(user_agent="LearnPython") for (name, location) in data: if location: try: location = geo_locator.geocode(location) except GeocoderTimedOut: continue if location: folium.Marker([location.latitude, location.longitude], popup=name).add_to(map) if __name__ == "__main__": map = folium.Map(location=[0, 0], zoom_start=2) location_data = get_tweets("#100DaysOfCode") put_markers(map, location_data) map.save("index.html")
This results in the following beautiful map.
Want to learn more Python? Also, check out my online course on Python.
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