BOKEH CHICAGO HOW TO
BOKEH CHICAGO INSTALL

It aims to be the fundamental high-level building block for doing practical, real world data analysis in Python”

pandas: “ Python package providing fast, flexible, and expressive data structures designed to make working with ‘relational’ or ‘labeled’ data both easy and intuitive.

The two main tools I will use for this analysis are pandas and Bokeh:

BOKEH CHICAGO HOW TO

This launches the Jupyter notebook dashboard in the browser (by default, For a quick rundown of how to use a jupyter notebook, see t his article. To launch the Jupyter notebook from the command line (in the desired directory), you type: jupyter notebook

BOKEH CHICAGO INSTALL

I have done that using the following commands at the command line: pip3 install pandas pip3 install bokeh You’ll also need to install python3, and the pandas / bokeh packages. Here are some instructions for installing Jupyter on your computer:

If you don’t know what a Jupyter notebook is, you are missing out! It’s an incredible visual way of programming / doing exploratory data analysis that has helped me become a better python programmer and has freed me of the shackles of Excel. The analysis I will perform is run using a Jupyter Notebook and python3, along with some other helpful packages ( pandas and Bokeh). I plan to dive into some more advanced topics in Part 2 that will include more advanced visualization methods using Bokeh and choropleth maps (Questions 3–4). This will be the main focus of this article (the answers to Questions 1–2). Are there any interesting trends in ridership I can see over time?Īs with most data science problems, the first place to start is to read in and process the data (and check for data quality problems) to begin answering some basic questions.What part of the city has the most ridership?.What are the most popular stations overall (and least popular)?.Which of the two train stations next to my apartment is more popular?.Some general questions I had about the CTA train system that this data will help to answer: Questions Overall, what is it you hope to discover? What are some specific questions you have before and after reviewing the data? What don’t you know that you would like to know? This process can be iterative: after discovering new information, you can start to ask new questions. Using this transit data, more concrete and data-informed patterns can be discovered.īefore jumping full-force into analyzing data and plotting curves, it is essential that you ask questions - or begin to explore the data. For example, you would know that taking your entire family on the Red Line State/Lake stop at 7:30AM would be a VERY BAD idea. By riding the trains frequently, you can get a sense for certain transit patterns. Formulating Questions / Exploring the DataĪs a Chicagoan I’ve ridden most of the train lines (except the purple line). We’ll use this more in Part 2 of this tutorial. You can export the information in a number of formats (JSON, TSV, XML). The second data set is a map, which contains geospatial data about each CTA train stop (including stop name, and coordinates on a map).

(weekday, Saturday or Sunday/Holiday) The awesome thing is this data is available all the way back to 2001. The first data set contains monthly ridership totals for all the CTA train stations, which are further broken down by day type.

CTA-System-Information-List-of-L-Stops-Map.

CTA - Ridership - ‘L’ Station Entries - Monthly Day-Type Averages & Totals.

The two datasets we will use for this tutorial are located below: The Chicago Data Portal has many datasets relating to the public transit system.

Bokeh chicago