Exploratory Data Analysis Of New York City Yellow Taxi Data

Posted on Jul 25, 2016
The skills the author demoed here can be learned through taking Data Science with Machine Learning bootcamp with NYC Data Science Academy.

Introduction :

The New York City Taxi & Limousine Commission has released staggeringly detailed historical data covering over 1.1 billion individual taxi trips in the city from January 2009 through June 2015. Taken as a whole, the detailed trip-level data is more than just a vast list of taxi pickup and drop off coordinates. It specifies some other useful information about the number of passengers, pick up times, location and revenue.

I chose this project to understand the dynamics of the yellow taxi industry better.  What kind of trips are made in cabs? Where do those trips occur? Does the number of passengers using the taxi follow any pattern? What are the predominant costs and locations of taxi trips, and what are the implications of these findings?

The primary goal of this analysis is to find useful insights to help the yellow taxi cab drivers  work smart not work hard.


About the Data :

The dataset includes trip records from all trips completed in yellow taxis from in NYC from January to June in 2015. Records include fields capturing pick-up and drop-off dates/times, pick-up and drop-off locations, trip distances, itemized fares, rate types, payment types, and driver-reported passenger counts.

The data has 77,080,575 rows and 11 columns


Pick Up Location For Yellow Taxi  in NYC:

Exploratory Data Analysis Of New York City Yellow Taxi Data

The pick up location for taxi is scattered all through Manhattan, and the intensity is high all around the city.

Data on Total Revenue Of The Industry Across The Week:

Exploratory Data Analysis Of New York City Yellow Taxi Data


This plot shows that Thursday, Friday and Saturday are the days where the business is at peak and Monday is the dullest. The surprising element to notice is that Wednesday has more customers than Sunday.


Data on Number of Passengers Travelling on Friday :

Exploratory Data Analysis Of New York City Yellow Taxi Data

Total Amount of Revenue Generated is directly proportional to Total Number of Passengers.

              The number of customers increases steadily from the start of the day and reaches its highest level at the end of the day. There is a slight increase in the passenger count at noon. This is seen in all weekdays and may be due to people going out for lunch. What makes Friday unusual is that there is continuing increase in taxi rides through the night - most likely because Friday is a night when people go out.


 Number of Passengers travelling on Saturday:



The day starts with high passenger count and decreases as the time passes. It reaches the lowest point at 6 am and then increases linearly till 3 pm. After 3 pm, there is no notable difference in the passenger count.  It looks like most people end their partying and are at home by 6am.



Data on Number of Passengers travelling on Sunday:



The number of passengers decreases from the start of the day till 6 am and increases from 6 am till 6 pm. Then, there is a continuous decrease till the end of the day. Perhaps New Yorker’s don’t go out on Sundays because they are resting after the heavy weekend partying; or maybe they are just preparing for the upcoming week.


Number of Passenger travelling from Monday to Thursday :


The Number of Passengers travelling increases continuously all day and reaches a peak from 4 pm to 6pm and decreases as the day comes to end.  Likely this  is because the people who work are most likely to complete their work between 4pm and 6pm, after which they tend to stay at home.  Afterall, it is a “school night”.  


Key Data Insights Obtained from the Project:

  • How much can the industry expect to earn from a day
  • Does the number of customers changes from start to the end of the day
  • How to plan the work schedule so that the driver works in best possible time and day and increase the his profit considerably


Future Scope:

  • Find the best and worst location to get customers at given time and day of the month
  • Analyze the location where people most likely go when boarding the taxi depending on their pickup location.
  • A model can be designed for increasing the growth of the companies in this field by making them spend their resources and money wisely

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