NYSE and Other Listings

core

Files Size Format Created Updated License Source
2 2MB csv public_domain_dedication_and_license NASDAQ official page
List of companies in the NYSE, and other exchanges. Data Data and documentation are available on NASDAQ's official webpage. Data is updated regularly on the FTP site. The file used in this repository: Other Exchanges Listed Securities Notes: Company Name is a parsed field using the Security read more
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Data Files

nyse-listed  

Field information

Field Name Order Type (Format) Description
ACT Symbol 1 string
Company Name 2 string

other-listed  

Field information

Field Name Order Type (Format) Description
ACT Symbol 1 string
Company Name 2 string
Security Name 3 string
Exchange 4 string
CQS Symbol 5 string
ETF 6 string
Round Lot Size 7 number
Test Issue 8 string
NASDAQ Symbol 9 string

Read me

List of companies in the NYSE, and other exchanges.

Data

Data and documentation are available on NASDAQ's official webpage. Data is updated regularly on the FTP site.

The file used in this repository:

Notes:

  • Company Name is a parsed field using the Security Name field.
  • Test Listings are excluded in the final dataset

Preparation

You need python plus pandas library tool installed to run the scripts. You also probably need to be on Linux/Unix or Mac for the shell scripts to work.

all datasets

Creates all csv files and datapackage.json

Run python script:

  python scripts/process.py

License

This Data Package is licensed by its maintainers under the Public Domain Dedication and License (PDDL).

Refer to the Copyright notice of the source dataset for any specific restrictions on using these data in a public or commercial product. Copyright © 2010, The NASDAQ OMX Group, Inc. All rights reserved.

Import into your tool

In order to use Data Package in R follow instructions below:

install.packages("devtools")
library(devtools)
install_github("hadley/readr")
install_github("ropenscilabs/jsonvalidate")
install_github("ropenscilabs/datapkg")

#Load client
library(datapkg)

#Get Data Package
datapackage <- datapkg_read("https://pkgstore.datahub.io/core/nyse-other-listings/latest")

#Package info
print(datapackage)

#Open actual data in RStudio Viewer
View(datapackage$data$"nyse-listed")
View(datapackage$data$"other-listed")

Tested with Python 3.5.2

To generate Pandas data frames based on JSON Table Schema descriptors we have to install jsontableschema-pandas plugin. To load resources from a data package as Pandas data frames use datapackage.push_datapackage function. Storage works as a container for Pandas data frames.

In order to work with Data Packages in Pandas you need to install our packages:

$ pip install datapackage
$ pip install jsontableschema-pandas

To get Data Package run following code:

import datapackage

data_url = "https://pkgstore.datahub.io/core/nyse-other-listings/latest/datapackage.json"

# to load Data Package into storage
storage = datapackage.push_datapackage(data_url, 'pandas')

# to see datasets in this package
storage.buckets

# you can access datasets inside storage, e.g. the first one:
storage[storage.buckets[0]]

In order to work with Data Packages in Python you need to install our packages:

$ pip install datapackage

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

import datapackage

dp = datapackage.DataPackage('https://pkgstore.datahub.io/core/nyse-other-listings/latest/datapackage.json')

# see metadata
print(dp.descriptor)

# get list of csv files
csvList = [dp.resources[x].descriptor['name'] for x in range(0,len(dp.resources))]
print(csvList) # ["resource name", ...]

# access csv file by the index starting 0
print(dp.resources[0].data)

To use this Data Package in JavaScript, please, follow instructions below:

Install datapackage using npm:

$ npm install [email protected]

Once the package is installed, use code snippet below


const Datapackage = require('datapackage').Datapackage

async function fetchDataPackageAndData(dataPackageIdentifier) {
  const dp = await new Datapackage(dataPackageIdentifier)
  await Promise.all(dp.resources.map(async (resource) => {
    if (resource.descriptor.format === 'geojson') {
      const baseUrl = resource._basePath.replace('/datapackage.json', '')
      const resourceUrl = `${baseUrl}/${resource._descriptor.path}`
      const response = await fetch(resourceUrl)
      resource.descriptor._values = await response.json()
    } else {
      // we assume resource is tabular for now ...
      const table = await resource.table
      // rows are simple arrays -- we can convert to objects elsewhere as needed
      const rowsAsObjects = false
      resource.descriptor._values = await table.read(rowsAsObjects)
    }
  }))

  // see the data package object
  console.dir(dp)

  // data itself is stored in Resource object, e.g. to access first resource:
  console.log(dp.resources[0]._values)

  return dp
}


fetchDataPackageAndData('https://pkgstore.datahub.io/core/nyse-other-listings/latest/datapackage.json');

Our JavaScript is written using ES6 features. We are using node.js v7.4.0 and passing --harmony option to enable ES6:

$ node --harmony index.js

In order to work with Data Packages in SQL you need to install our packages:

$ pip install datapackage
$ pip install jsontableschema-sql
$ pip install sqlalchemy

To import Data Package to your SQLite Database, run following code:

import datapackage
from sqlalchemy import create_engine

data_url = 'https://pkgstore.datahub.io/core/nyse-other-listings/latest/datapackage.json'
engine = create_engine('sqlite:///:memory:')

# to load Data Package into storage
storage = datapackage.push_datapackage(data_url, 'sql', engine=engine)

# to see datasets in this package
storage.buckets

# to execute sql command (assuming data is in "data" folder, name of resource is data and file name is data.csv)
storage._Storage__connection.execute('select * from data__data___data limit 1;').fetchall()

# description of the table columns
storage.describe('data__data___data')