The Data Scientist‘s Guide to Finding the Best Meetup Groups

Introduction

For data scientists, meetup groups are a great way to learn new skills, network with peers, and stay up-to-date on the latest industry trends. However, with thousands of data science and analytics-related groups on Meetup.com, it can be daunting to figure out which ones are worth your time. Like many repetitive tasks, the process of sifting through groups to find the best ones is a perfect candidate for some data science-powered automation.
In this guide, we‘ll walk through how to use Python to programmatically access data on Meetup groups via API, analyze that data to determine the best groups to join, and visualize the findings. Whether you‘re looking for local groups to attend in-person or virtual groups to participate in from anywhere, these techniques will help you zero in on the most active and well-regarded Meetups in your areas of interest.

Meetup.com and its API

Meetup is an online platform that facilitates in-person and virtual community events. Users can join groups related to their interests, organized around a particular location, topic, industry, etc. Groups on the site are created by volunteer organizers who plan the events, with group members signing up to attend those that interest them.
For data science purposes, Meetup provides a free API that allows access to public data on groups, events, members, and other site information. As of 2024, the main Meetup API endpoints of interest are:
– `/find/groups`: Provides a stream of Meetup groups based on location, category, and other search criteria
– `/events`: Provides a list of past and upcoming Meetup events for a given group or in a given location/category
– `/members`: Provides a list of members in a given Meetup group
The API can return data in JSON or XML format. API requests require an API key, which is provided to anyone who signs up for a Meetup account.

Finding the best data science Meetup groups with Python

Now let‘s put the Meetup API to work to find the best data science groups worldwide. We‘ll determine "best" by looking at things like group size, frequency of events, recent activity, and member ratings.

Step 1: Set up environment and import libraries

First make sure you have Python and key data science libraries installed, including pandas, matplotlib, and requests. If you don‘t have them already, you can install with pip:
“`
pip install pandas matplotlib requests
“`
Then import the libraries we‘ll be using:
“`
import pandas as pd
import matplotlib.pyplot as plt
import requests
“`

Step 2: Configure API parameters

Next specify the parameters for your API queries, including your API key, the maximum number of results to return per query, the countries to search, and basic search criteria like only wanting to see groups related to data science.
“`
api_key = ‘your_api_key_here‘
page_size = 200
country_list = [‘US‘, ‘UK‘, ‘India‘, ‘Australia‘, ‘China‘] category_ids = [183] # see Meetup API docs for category IDs; 183 = ‘Data Science‘
“`

Step 3: Query the API

Now you‘re ready to send some queries! Loop through the country list and for each one, use the `/find/groups` endpoint to get a list of matching groups. Be sure to include your search parameters in the URL string.
“`
group_data = [] for country in country_list:
params = {‘key‘: api_key, ‘country‘: country, ‘category‘: category_ids,
‘page‘: page_size, ‘only‘: ‘id,name,city,country,members,rating‘}
r = requests.get(‘https://api.meetup.com/find/groups‘, params=params)
r.raise_for_status() # check for errors
for group in r.json():
group[‘members‘] = group[‘members‘] or 0
group_data.append(group)

df = pd.DataFrame(group_data)
print(df.head())

This gives you a DataFrame with key metadata for matching groups in each of the target countries. The `members` column shows the group size, while `rating` is the average star rating out of 5 from members.
<h3>Step 4: Get event data</h3>
Group size and ratings are useful, but to really gauge how active and worthwhile a Meetup group is, we need to look at its events. Let‘s go back to the API to get more details on each group‘s event frequency and recency.

eventdata = [] for , group in df.iterrows():
params = {‘key‘: api_key, ‘group_id‘: group[‘id‘], ‘status‘: ‘past,upcoming‘}
r = requests.get(‘https://api.meetup.com/events‘, params=params)
r.raise_for_status()
events = r.json()
if events:
event_data.append({
‘id‘: group[‘id‘],
‘events‘: len(events),
‘last_event‘: max(e[‘time‘] for e in events),
‘next_event‘: min(e[‘time‘] for e in events if e[‘status‘] == ‘upcoming‘)
if any(e[‘status‘] == ‘upcoming‘ for e in events) else None
})

event_df = pd.DataFrame(event_data).set_index(‘id‘)
df = df.join(event_df, on=‘id‘)

This adds columns to the DataFrame showing the total number of events found for each group, the timestamp of the most recent past event, and the timestamp of the next upcoming event if any.
<h3>Step 5: Analyze the data</h3>
With our API data collected, we can finally start to draw some conclusions about which Meetup groups seem most promising. Let‘s define "best" as groups that are:
- Large (>500 members)
- Active (>10 events in the last year)
- Well-regarded (average rating >4.5 stars)
We can use pandas methods to filter the DataFrame down to just groups meeting those criteria:

cutoff_date = pd.to_datetime(‘today‘) – pd.to_timedelta(365, unit=‘d‘)
best_groups = df[(df.members > 500) &
(df.events > 10) &
(df.rating >= 4.5) &
(df.last_event > cutoff_date)] print(f‘Found {len(best_groups)} top data science Meetup groups‘)
best_groups[[‘name‘, ‘city‘, ‘country‘, ‘members‘, ‘rating‘, ‘events‘]]

<h3>Step 6: Visualize the results</h3>
To make the results easier to parse, let‘s generate some visualizations. Here‘s one showing the number of top data science Meetup groups found in each country:

plt.figure(figsize=(8,5))
best_groups.groupby(‘country‘).size().plot.bar()
plt.xlabel(‘Country‘)
plt.ylabel(‘Number of Top Meetup Groups‘)
plt.title(‘Data Science Meetup Groups by Country‘)
plt.show()

And another plotting the number of members in each top group, color-coded by country:

plt.figure(figsize=(10,5))
with pd.plotting.plot_params.use(‘x_compat‘, True):
for country, df_country in best_groups.groupby(‘country‘):
df_country.set_index(‘name‘)[‘members‘].sort_values(ascending=False).plot.bar(color=f‘C{list(country_list).index(country)}‘, label=country)
plt.xlabel(‘Meetup Group‘)
plt.ylabel(‘Members‘)
plt.title(‘Top Data Science Meetup Groups by Membership‘)
plt.legend()
plt.gcf().axes[0].tick_params(axis=‘x‘, rotation=45)
plt.show()

<h2>Tips and Extensions</h2>
Now that you‘ve seen the basic approach, here are a few ways you can adapt it to your own needs:
- Adjust the search parameters, like target countries and category IDs, to find groups more specific to your location and interests. For example, you could look for groups focused on a particular programming language or using `find/locations` to constrain results to your city.
- Modify the best group criteria to select for other factors that are important to you, like frequency of events or how soon the next event is happening. You may need to collect additional data fields from the API to enable some criteria.
- Persist the data in a database or file so you can track changes over time. With a time series of group stats, you could identify fast-growing groups, or those with an engaged base of repeat event attendees.
- Incorporate data from other sources to add context around the groups. For example, you could cross-reference the members list against your LinkedIn connections, or analyze the text of event descriptions to determine popular discussion topics.
<h2>Conclusion</h2>
This guide demonstrated how to leverage APIs and basic data science tools to streamline a common challenge for data professionals. By using Python to automatically find the biggest, most active, and highest rated Meetup groups, you can spend less time searching and more time participating in valuable communities.
The techniques shown here only scratch the surface of what‘s possible. I encourage you to explore further and see what other insights you can uncover in Meetup data. Feel free to share any interesting findings or creative applications you come up with!