Saeed Falana Ramallah, Palestine
This project used the following dataset, which are available in (Datasource: Ford goBike )
The dataset is a huge file so I compressed the file. So, before running my analysis, you should decompress the file.
| Column Name | Number of Rows | Explanation |
|---|---|---|
| Unnamed: 0 1538086 non-null int64 | ||
| Unnamed: 0.1 519700 non-null float64 | ||
bike_id |
1,538,086 | This dataset doesn't have 'user_id' |
duration_sec |
1,538,086 | Trip duration recorded in second |
end_station_id |
1,532,841 | There are some dockless rides |
end_station_latitude |
1,538,086 | |
end_station_longitude |
1,538,086 | |
end_station_name |
1,532,841 | There are some dockless rides |
end_time |
1,538,086 | |
start_station_id |
1,532,841 | There are some dockless rides |
start_station_latitude |
1,538,086 | |
start_station_longitude |
1,538,086 | |
start_station_name |
1,532,841 | There are some dockless rides |
start_time |
1,538,086 | |
bike_share_for_all_trip |
1,018,386 | This service started on Jan 2018 |
user_type |
1,538,086 | Either subscriber or single-rider |
member_gender |
1,018,386 | This has some missing values |
member_birth_year |
1,538,086 | No missing value |
The project is mainly written in Python 3.6. I used pandas and numpy for data analysis, and used seaborn, and matplotlib for data visualization.
from requests import get
from os import path, getcwd, makedirs, listdir
from io import BytesIO
from zipfile import ZipFile
import pandas as pd
import numpy as np
import matplotlib
from matplotlib import pyplot as plt
import matplotlib.ticker as tick
import seaborn as sns
import datetime
import math
import calendar
import cufflinks as cf
import warnings
warnings.filterwarnings('ignore')
from IPython.display import Image
from bokeh.io import output_file, show, output_notebook
from bokeh.models import ColumnDataSource, GMapOptions
from bokeh.plotting import gmap, figure
%matplotlib inline
%load_ext rpy2.ipython
1. How does subscibers and customers behave differently?
Subscribers' rides take place around both morning commute time and evening commute time. On the contrary, customers' rides take place the most during weekend, which represents the main purpose for the user type is different. The former is for convenience around commute time and the latter is more likely for leisure.
2. Which age group favors e-bike more?
Suprisingly, 10 to 20 years user group seems to show the most interest in the e-bike, seeing that the percentage of e-bike rides of all the rides by 10 to 20 years old users is around 10%. Also, the younger a rider is, the more likely the rider would be fond of electric bike rides.
3. What are the most popular docks for 'Bike share for all' members?
'Bike share for all' program is a subscription model for low income bay area residents. Unlike most popular docks for all members gathered around financial distinct and market street, the popular docks for 'Bike share for all' members are sqreaded especially around bart station or caltrain station, even though those are quite further away from market street, such as 24th street bart station. I guess it's because low income people tend to travel further from south bay or east bay and they might take public transportation to work around San Fancisco area.