Uber has been a major source of travel for people living in urban areas. Some people don’t have their vehicles while some don’t drive their vehicles intentionally because of their busy schedule. So different kinds of people are using the services of Uber and other taxi services. In this article, I will take you through Uber trips analysis using Python.
Uber Trips Analysis
By analyzing Uber trips, we can draw many patterns like which day has the highest and the lowest trips or the busiest hour for Uber and many other patterns. The dataset I’m using here is based on Uber trips from New York, a city with a very complex transportation system with a large residential community.
The dataset contains data of about 4.5 million uber pickups in New York City from April to September and 14.3 million pickups from January to June 2015. You can do so much more with this dataset rather than just analyzing it. But for now, in the section below, I will take you through Uber Trips analysis using Python.
Uber Trips Analysis using Python
I will start this task of Uber trips analysis by importing the necessary Python libraries and the dataset:
Date/Time Lat Lon Base 0 2014-09-01 00:01:00 40.2201 -74.0021 B02512 1 2014-09-01 00:01:00 40.7500 -74.0027 B02512 2 2014-09-01 00:03:00 40.7559 -73.9864 B02512 3 2014-09-01 00:06:00 40.7450 -73.9889 B02512 4 2014-09-01 00:11:00 40.8145 -73.9444 B02512
This data contains data about date and time, latitude and longitude, and a Base column that contains code affiliated with the uber pickup. You can get more datasets for the task of Uber trips analysis from here, for now, let’s prepare the data that I am using here to analyze the Uber trips according to days and hours:
Date/Time Lat Lon Base Day Weekday Hour 0 2014-09-01 00:01:00 40.2201 -74.0021 B02512 1 0 0 1 2014-09-01 00:01:00 40.7500 -74.0027 B02512 1 0 0 2 2014-09-01 00:03:00 40.7559 -73.9864 B02512 1 0 0 3 2014-09-01 00:06:00 40.7450 -73.9889 B02512 1 0 0 4 2014-09-01 00:11:00 40.8145 -73.9444 B02512 1 0 0
So I have prepared this data according to the days and hours, as I am using the Uber trips for the September month so let’s have a look at each day to see on which day the Uber trips were highest:
sns.set(rc={'figure.figsize':(12, 10)})
sns.distplot(data["Day"])
By looking at the daily trips we can say that the Uber trips are rising on the working days and decreases on the weekends. Now let’s analyze the Uber trips according to the hours:
sns.distplot(data["Hour"])
According to the hourly data, the Uber trips decreases after midnight and then start increasing after 5 am and the trips keep rising till 6 pm such that 6 pm is the busiest hour for Uber then the trips start decreasing. Now let’s analyze the Uber trips according to the weekdays:
sns.distplot(data["Weekday"])
In the above figure 0 indicates Sunday, on Sundays the Uber trips and more than Saturdays so we can say people also use Uber for outings rather than for just going to work. On Saturdays, the Uber trips are the lowest and on Mondays, they are the highest. Now let’s have a look at the correlation of hours and weekdays on the Uber trips:
As we are having the data about longitude and latitude so we can also plot the density of Uber trips according to the regions of the New Your city:
Summary
So this is how we can analyze the Uber trips for New York City. Some of the conclusions that I got from this analysis are:
- Monday is the most profitable day for Uber
- On Saturdays less number of people use Uber
- 6 pm is the busiest day for Uber
- On average a rise in Uber trips start around 5 am.
- Most of the Uber trips originate near the Manhattan region in New York.
I hope you liked this article on Uber trips analysis using Python. Feel free to ask your valuable questions in the comments section below.
