SMDG Master Terminal Facilities List


Files Size Format Created Updated License Source
2 503kB csv zip 3 months ago John Snow Labs Standard License John Snow Labs SMDG

Data Files

File Description Size Last changed Download
smdg-master-terminal-facilities-list-csv 86kB csv (86kB) , json (310kB)
smdg-master-terminal-facilities-list_zip Compressed versions of dataset. Includes normalized CSV and JSON data with original data and datapackage.json. 81kB zip (81kB)


This is a preview version. There might be more data in the original version.

Field information

Field Name Order Type (Format) Description
UN_LOCODE 1 string Main location UN/LOCODE. In an UN/EDIFACT message, used in a LOC segment, element C517.3225
Alternative_UN_LOCODE 2 string Alternative Main location UN/LOCODE. In an UN/EDIFACT message, used in a LOC segment, element C517.3225
Terminal_Code 3 string In an UN/EDIFACT message, used in a LOC segment, element C519.3223
Terminal_Facility 4 string Terminal where facility is provided.
Company_Name 5 string Name of company.
Last_Change 6 string Last change of this entry.
Valid_From 7 date (%Y-%m-%d) Entry is valid from this date.
Valid_Before 8 date (%Y-%m-%d) Entry is valid till this date.
Applicant_Name 9 string Name of applicant.
Applicant_Email 10 string Email address of applicant.
Latitude 11 number Latitude for the facility code.
Longitude 12 number Longitude for the facility code.
Terminal_Contact_Or_Website 13 string Contact number or website of terminal.

Import into your tool

Data-cli or just data is the program to get and post your data with the datahub.
Use data with the almost like you use git with the github. Here are installation instructions.

data get
tree JohnSnowLabs/smdg-master-terminal-facilities-list
# Get a list of dataset's resources
curl -L -s | grep path

# Get resources

curl -L

curl -L

If you are using R here's how to get the data you want quickly loaded:

install.packages("jsonlite", repos="")

json_file <- ''
json_data <- fromJSON(paste(readLines(json_file), collapse=""))

# get list of all resources:

# print all tabular data(if exists any)
for(i in 1:length(json_data$resources$datahub$type)){
    path_to_file = json_data$resources$path[i]
    data <- read.csv(url(path_to_file))

Note: You might need to run the script with root permissions if you are running on Linux machine

Install the Frictionless Data data package library and the pandas itself:

pip install datapackage
pip install pandas

Now you can use the datapackage in the Pandas:

import datapackage
import pandas as pd

data_url = ''

# to load Data Package into storage
package = datapackage.Package(data_url)

# to load only tabular data
resources = package.resources
for resource in resources:
    if resource.tabular:
        data = pd.read_csv(resource.descriptor['path'])
        print (data)

For Python, first install the `datapackage` library (all the datasets on DataHub are Data Packages):

pip install datapackage

To get Data Package into your Python environment, run following code:

from datapackage import Package

package = Package('')

# print list of all resources:

# print processed tabular data (if exists any)
for resource in package.resources:
    if resource.descriptor['datahub']['type'] == 'derived/csv':

If you are using JavaScript, please, follow instructions below:

Install data.js module using npm:

  $ npm install data.js

Once the package is installed, use the following code snippet:

const {Dataset} = require('data.js')

const path = ''

// We're using self-invoking function here as we want to use async-await syntax:
;(async () => {
  const dataset = await Dataset.load(path)
  // get list of all resources:
  for (const id in dataset.resources) {
  // get all tabular data(if exists any)
  for (const id in dataset.resources) {
    if (dataset.resources[id]._descriptor.format === "csv") {
      const file = dataset.resources[id]
      // Get a raw stream
      const stream = await
      // entire file as a buffer (be careful with large files!)
      const buffer = await file.buffer
      // print data