metra

Package Dependencies | Setup | Getting Started | Example Usage

Unofficial Python wrapper for interacting with the Metra GTFS API

Package Dependencies

This repository relies heavily on Pandas. Aside from that, it is recommended that you use Python 3.8+ since the following modules from the standard library are utilized as well:

• io
• zipfile
• requests

Things to note:

• API credentials are required. Obtain your personal API Key and Secret Key by filling out the form at the Metra Developer Home Page
• This library depends on the use of static data that Metra updates once a day. When the StaticAPI class is initiated, it will check to make ensure the data on your local drive is up to date. Data is stored in a ~20 KB zip file within the packaged directory

Setup

1. Download the repository

# clone the repo with git
git clone https://github.com/joerex1418/metra.git

2. Setup your API Credentials

The Metra GTFS API Feed utilizes basic authentication for accessing the data. Once you have your API credentials, create a keys.txt file in the library's root directory (where the __init__.py file is located).

On the first line in your keys.txt file, type in your assigned API Key. On the second line, put in your assigned Secret Key

keys.txt Format

<YOUR_API_KEY_HERE>
<YOUR_SECRET_KEY_HERE>

Save this file and you should be all set.

Getting Started

import metra

s = metra.StaticAPI()

Example Usage

# Stops Dataframe
>>> df = s.stops()
>>> df.head()
        stop_id             stop_name   stop_lat   stop_lon zone_id  wheelchair_boarding
0        GENEVA                Geneva  41.881667 -88.310000       H                    1
1      WCHICAGO          West Chicago  41.881111 -88.198889       F                    1
2      WINFIELD              Winfield  41.870000 -88.156944       F                    1
3       WHEATON               Wheaton  41.864444 -88.111944       E                    1
4    COLLEGEAVE           College Ave  41.868333 -88.090278       E                    1

# Next Train Departures (leaving the Naperville station, going to Chicago Union Station)
>>> df = s.next_trains("NAPERVILLE","CUS")
>>> df[["trip_id","arrival_time","stop_id"]].head()

            trip_id        arrival_time     stop_id
0  BNSF_BN1260_V2_B 2022-08-13 13:15:00  NAPERVILLE
1  BNSF_BN1262_V2_B 2022-08-13 14:15:00  NAPERVILLE
2  BNSF_BN2018_V2_B 2022-08-13 14:33:00  NAPERVILLE
3  BNSF_BN1264_V2_B 2022-08-13 15:15:00  NAPERVILLE
4  BNSF_BN2020_V2_B 2022-08-13 15:35:00  NAPERVILLE

# Search for stop entries from the "stops" dataset
>>> s.stop_search(query="clybourn")
     stop_id stop_name   stop_lat   stop_lon zone_id  wheelchair_boarding
55  CLYBOURN  Clybourn  41.916944 -87.668056       A                    0