Cyclistic - How does a bike-share navigate speedy success?

Author

Zimuzo Obi

Published

August 29, 2025

1 Background

Cyclistic launched its bike-share program in 2016, growing to 5,824 bikes across 692 Chicago stations. Riders can unlock a bike at one station and return it to another. The program offers flexible pricing: single-ride and day passes (casual riders) and annual memberships (members).

2 Objective

This analysis explores historical trip data to compare casual riders and members, aiming to uncover trends that can inform marketing strategies to convert casual riders into members.

2.1 Key Question

How do annual members and casual riders use Cyclistic bikes differently?

3 Data Sources

The data has been made available by Motivate International Inc. under this licence. Download the data here: 12 months (year 2021) of Cyclistic trip data

4 Analysis(data prep with supporting visuals)

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

trip02 = pd.read_csv('files/202102-divvy-tripdata.csv')
trip03 = pd.read_csv('files/202103-divvy-tripdata.csv')
trip01 = pd.read_csv('files/202101-divvy-tripdata.csv')
trip04 = pd.read_csv('files/202104-divvy-tripdata.csv')
trip05 = pd.read_csv('files/202105-divvy-tripdata.csv')
trip06 = pd.read_csv('files/202106-divvy-tripdata.csv')
trip07 = pd.read_csv('files/202107-divvy-tripdata.csv')
trip08 = pd.read_csv('files/202108-divvy-tripdata.csv')
trip09 = pd.read_csv('files/202109-divvy-tripdata.csv')
trip10 = pd.read_csv('files/202110-divvy-tripdata.csv')
trip11 = pd.read_csv('files/202111-divvy-tripdata.csv')
trip12 = pd.read_csv('files/202112-divvy-tripdata.csv')

# Combine all the data
all_trips = pd.concat([trip01, trip02, trip03, trip04, trip05, trip06, trip07, trip08, trip09, trip10, trip11, trip12])

all_trips.info()
all_trips.shape

<class 'pandas.core.frame.DataFrame'>
Index: 5595063 entries, 0 to 247539
Data columns (total 13 columns):
 #   Column              Dtype  
---  ------              -----  
 0   ride_id             object 
 1   rideable_type       object 
 2   started_at          object 
 3   ended_at            object 
 4   start_station_name  object 
 5   start_station_id    object 
 6   end_station_name    object 
 7   end_station_id      object 
 8   start_lat           float64
 9   start_lng           float64
 10  end_lat             float64
 11  end_lng             float64
 12  member_casual       object 
dtypes: float64(4), object(9)
memory usage: 597.6+ MB

(5595063, 13)

all_trips.drop(['start_station_id', 'start_lat', 'start_lng', 'end_lat', 'end_lng', 'end_station_id'], axis=1, inplace=True)

df = all_trips.copy()

df['started_at'] = pd.to_datetime(df['started_at'])
df['ended_at'] = pd.to_datetime(df['ended_at'])

mask = df['ended_at'] < df['started_at']
df.loc[mask, 'ended_at'] += pd.Timedelta(days=1)

df['ride_duration'] = round((df['ended_at'] - df['started_at']).dt.total_seconds() / 60 , 2)

df['month'] = df['started_at'].dt.month_name()
df['day'] = df['started_at'].dt.day_name()

df.head(3)

	ride_id	rideable_type	started_at	ended_at	start_station_name	end_station_name	member_casual	ride_duration	month	day
0	E19E6F1B8D4C42ED	electric_bike	2021-01-23 16:14:19	2021-01-23 16:24:44	California Ave & Cortez St	NaN	member	10.42	January	Saturday
1	DC88F20C2C55F27F	electric_bike	2021-01-27 18:43:08	2021-01-27 18:47:12	California Ave & Cortez St	NaN	member	4.07	January	Wednesday
2	EC45C94683FE3F27	electric_bike	2021-01-21 22:35:54	2021-01-21 22:37:14	California Ave & Cortez St	NaN	member	1.33	January	Thursday

avg_ride_duration = round(df['ride_duration'].mean(), 2)
print(f"The average ride duration is {avg_ride_duration} minutes.")

The average ride duration is 21.97 minutes.

# // Average ride between membership types

sns.barplot(x=df['member_casual'], y=df['ride_duration'], palette="pastel", hue=df['member_casual'], errorbar=None)
plt.title('Average Ride Duration by Membership Type')
plt.xlabel('Membership Type')
plt.ylabel('Average Ride Duration (minutes)')
plt.show()

# Different types of rideable bikes
sns.countplot(x='rideable_type', hue='member_casual', data=df, palette="pastel")
plt.title('Rideable Type by Membership Type')
plt.xlabel('Rideable Type')
plt.ylabel('Count') 
plt.show()

# Rented bike distribution

plt.pie(df['rideable_type'].value_counts(), labels=df['rideable_type'].unique(), autopct='%1.1f%%', colors=['#A1C9F4', '#FFB482', "#DBDADA"])
plt.title('Rented Bike Distribution')
plt.show()

# Rides per month
plt.figure(figsize=(12, 6))
sns.countplot(x=df['month'], hue=df['member_casual'], palette="pastel", order=df['month'])
plt.title('Rides per Month by Membership Type')
plt.xlabel('Month')
plt.ylabel('Number of Rides') 

plt.show()

# Rides per day of the week
plt.figure(figsize=(8, 6))
sns.countplot(x=df['day'], hue=df['member_casual'], palette="pastel", order=['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday'])
plt.title('Rides per day by Membership Type')
plt.xlabel('Day of the Week')
plt.ylabel('Number of Rides') 

plt.show()

5 Summary of Analysis

Usage Patterns:
On average, casual riders utilize bikes more frequently than annual members.

Bike Preferences:
Both casual and member users predominantly use classic and electric bikes, with minimal usage of docked bikes. Notably, annual members did not use docked bikes at all.

Seasonal Trends:
Bike rentals peak during the summer months, from June to September, with elevated usage extending into October.

Day-of-Week Trends:
Member users tend to ride more frequently on weekdays, while casual users show a significant increase in activity during weekends.

6 Recommendations

Weekend Membership Plan: Introduce a weekend-specific membership plan to attract casual riders who predominantly use bikes during weekends.

Seasonal Discounts: Offer discounted annual membership plans during the summer months to capitalize on the peak rental season and encourage conversions from casual riders.

Incentivized Returns: Implement a points-based system for annual members, rewarding riders who return bikes to designated stations. Points can be redeemed for discounts or benefits, encouraging better usage of docked bikes and targeting casual users to transition into membership plans. This recommendation stems from observations of many bikes not being returned to stations.