Jamison  Fisher

Jamison Fisher


Exploring Bike Share Data

How to access trip data from Citi Bike and use Python and Pandas to prepare it for analysis

Many bike share systems make available their trip data for those who want to understand how their systems are used. The bike share system in New York City, Citi Bike, is one of them, but they don’t provide much more than the data. I’ve got some experience in obtaining and preparing their data for visualization, so in this article I will show you how to get started with this rich data source.

In the Before Times I commuted from suburban New Jersey to my job as a Product Manager in New York City at an office, now shuttered, above Penn Station. To get around in the City at lunch or after work I often relied on Citi Bike, New York’s bike share system. I found I could get to destinations in midtown and even further afield faster than walking and cheaper than the bus or subway. When I discovered that Citi Bike made trip data publicly available I thought that it might provide an interesting use case for the data preparation product that I managed.

Using real data turned out to be much more interesting then the sample files that we had been using because there were actual anomalies that needed to be cleaned up to make the data useful for analysis, and there were interesting stories to tell from the data.

The trip data files contain one record for each ride, around two million records per month, depending on the season. It’s a traditional bike share system with fixed stations where a user picks up a bike at one dock, using a key fob or a code, and returns it at another. The station and time when the ride started and stopped is recorded for each ride.

Some limited information about the rider is also recorded: their gender and year of birth. Citi Bike also distinguishes between what they call Subscribers who buy an annual pass (current cost is $179 for unlimited rides up to 45 minutes) and Customers who buy a day pass ($15 for unlimited 30 minute rides) or a single ride pass ($3).

For each user type there are overage fees for longer rides. For Customers it’s $4 per 15 minutes; for Subscribers it’s $0.15 per minute. These fees seem to be designed to discourage longer rides, more so than to increase revenue.

The Citi Bike System Data page describes the information provided. The specific information for each ride is:

  • Trip Duration (seconds)
  • Start Time and Date
  • Stop Time and Date
  • Start Station Name
  • End Station Name
  • Station ID
  • Station Lat/Long
  • Bike ID
  • User Type (Customer = 24-hour pass or single ride user; Subscriber = Annual Member)
  • Gender (Zero=unknown; 1=male; 2=female)
  • Year of Birth

The kinds of questions we wanted to answer included ones like these: What’s the most common ride duration? What times of the day does the system get the most usage? How much does ridership vary over the course of a month? What are the most used stations? How old are the riders?

While the answers to these questions can be found in the trip data files, the data needs to be augmented to provide easy answers. For example the trip duration in seconds is too granular; minutes would be more useful.

Over the years I used this data for numerous presentations to customers and at user group meetings. And the cleansed data I created was used by the product managers for a visualization tool for their own presentations.

When I happened to use Jupyter Notebook, Python and Pandas for another project I decided to see what it would take to prepare the Citi Bike trip data using these tools.

Jupyter Notebook is an open-source web-based application that allows you to create and share documents that contain code, visualizations and narrative text. It’s commonly used for data preparation and visualization but has many other uses as well. **Python **is the programming language used by default and Pandas is a software library widely used for data manipulation and analysis. I also used **Seaborn **as an easy way to visualize the data.

The Jupyter Notebook with all the code and output can be found on github.

#pandas #bike-sharing #data-science #seaborn #python

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Exploring Bike Share Data
 iOS App Dev

iOS App Dev


Your Data Architecture: Simple Best Practices for Your Data Strategy

If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.

If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.

In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.

#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition

Gerhard  Brink

Gerhard Brink


Getting Started With Data Lakes

Frameworks for Efficient Enterprise Analytics

The opportunities big data offers also come with very real challenges that many organizations are facing today. Often, it’s finding the most cost-effective, scalable way to store and process boundless volumes of data in multiple formats that come from a growing number of sources. Then organizations need the analytical capabilities and flexibility to turn this data into insights that can meet their specific business objectives.

This Refcard dives into how a data lake helps tackle these challenges at both ends — from its enhanced architecture that’s designed for efficient data ingestion, storage, and management to its advanced analytics functionality and performance flexibility. You’ll also explore key benefits and common use cases.


As technology continues to evolve with new data sources, such as IoT sensors and social media churning out large volumes of data, there has never been a better time to discuss the possibilities and challenges of managing such data for varying analytical insights. In this Refcard, we dig deep into how data lakes solve the problem of storing and processing enormous amounts of data. While doing so, we also explore the benefits of data lakes, their use cases, and how they differ from data warehouses (DWHs).

This is a preview of the Getting Started With Data Lakes Refcard. To read the entire Refcard, please download the PDF from the link above.

#big data #data analytics #data analysis #business analytics #data warehouse #data storage #data lake #data lake architecture #data lake governance #data lake management

Cyrus  Kreiger

Cyrus Kreiger


How Has COVID-19 Impacted Data Science?

The COVID-19 pandemic disrupted supply chains and brought economies around the world to a standstill. In turn, businesses need access to accurate, timely data more than ever before. As a result, the demand for data analytics is skyrocketing as businesses try to navigate an uncertain future. However, the sudden surge in demand comes with its own set of challenges.

Here is how the COVID-19 pandemic is affecting the data industry and how enterprises can prepare for the data challenges to come in 2021 and beyond.

#big data #data #data analysis #data security #data integration #etl #data warehouse #data breach #elt

Macey  Kling

Macey Kling


Applications Of Data Science On 3D Imagery Data

CVDC 2020, the Computer Vision conference of the year, is scheduled for 13th and 14th of August to bring together the leading experts on Computer Vision from around the world. Organised by the Association of Data Scientists (ADaSCi), the premier global professional body of data science and machine learning professionals, it is a first-of-its-kind virtual conference on Computer Vision.

The second day of the conference started with quite an informative talk on the current pandemic situation. Speaking of talks, the second session “Application of Data Science Algorithms on 3D Imagery Data” was presented by Ramana M, who is the Principal Data Scientist in Analytics at Cyient Ltd.

Ramana talked about one of the most important assets of organisations, data and how the digital world is moving from using 2D data to 3D data for highly accurate information along with realistic user experiences.

The agenda of the talk included an introduction to 3D data, its applications and case studies, 3D data alignment, 3D data for object detection and two general case studies, which are-

  • Industrial metrology for quality assurance.
  • 3d object detection and its volumetric analysis.

This talk discussed the recent advances in 3D data processing, feature extraction methods, object type detection, object segmentation, and object measurements in different body cross-sections. It also covered the 3D imagery concepts, the various algorithms for faster data processing on the GPU environment, and the application of deep learning techniques for object detection and segmentation.

#developers corner #3d data #3d data alignment #applications of data science on 3d imagery data #computer vision #cvdc 2020 #deep learning techniques for 3d data #mesh data #point cloud data #uav data

Uriah  Dietrich

Uriah Dietrich


What Is ETLT? Merging the Best of ETL and ELT Into a Single ETLT Data Integration Strategy

Data integration solutions typically advocate that one approach – either ETL or ELT – is better than the other. In reality, both ETL (extract, transform, load) and ELT (extract, load, transform) serve indispensable roles in the data integration space:

  • ETL is valuable when it comes to data quality, data security, and data compliance. It can also save money on data warehousing costs. However, ETL is slow when ingesting unstructured data, and it can lack flexibility.
  • ELT is fast when ingesting large amounts of raw, unstructured data. It also brings flexibility to your data integration and data analytics strategies. However, ELT sacrifices data quality, security, and compliance in many cases.

Because ETL and ELT present different strengths and weaknesses, many organizations are using a hybrid “ETLT” approach to get the best of both worlds. In this guide, we’ll help you understand the “why, what, and how” of ETLT, so you can determine if it’s right for your use-case.

#data science #data #data security #data integration #etl #data warehouse #data breach #elt #bid data