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S&P 500 Companies with Financial Information

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core

Files Size Format Created Updated License Source
2 61kB csv zip 1 year ago 4 months ago Open Data Commons Public Domain Dedication and License v1.0
List of companies in the S&P 500 (Standard and Poor's 500). The S&P 500 is a free-float, capitalization-weighted index of the top 500 publicly listed stocks in the US (top 500 by market cap). The dataset includes a list of all the stocks contained therein. Data Information on S&P 500 index used read more
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Data Files

Download files in this dataset

File Description Size Last changed Download
constituents 19kB csv (19kB) , json (37kB)
s-and-p-500-companies_zip Compressed versions of dataset. Includes normalized CSV and JSON data with original data and datapackage.json. 23kB zip (23kB)

constituents  

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Field information

Field Name Order Type (Format) Description
Symbol 1 string
Name 2 string
Sector 3 string

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Integrate this dataset into your favourite tool

Use our data-cli tool designed for data wranglers:

data get https://datahub.io/core/s-and-p-500-companies
data info core/s-and-p-500-companies
tree core/s-and-p-500-companies
# Get a list of dataset's resources
curl -L -s https://datahub.io/core/s-and-p-500-companies/datapackage.json | grep path

# Get resources

curl -L https://datahub.io/core/s-and-p-500-companies/r/0.csv

curl -L https://datahub.io/core/s-and-p-500-companies/r/1.zip

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

install.packages("jsonlite", repos="https://cran.rstudio.com/")
library("jsonlite")

json_file <- 'https://datahub.io/core/s-and-p-500-companies/datapackage.json'
json_data <- fromJSON(paste(readLines(json_file), collapse=""))

# get list of all resources:
print(json_data$resources$name)

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

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 = 'https://datahub.io/core/s-and-p-500-companies/datapackage.json'

# 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('https://datahub.io/core/s-and-p-500-companies/datapackage.json')

# print list of all resources:
print(package.resource_names)

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

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 = 'https://datahub.io/core/s-and-p-500-companies/datapackage.json'

// 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) {
    console.log(dataset.resources[id]._descriptor.name)
  }
  // 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 file.stream()
      // entire file as a buffer (be careful with large files!)
      const buffer = await file.buffer
      // print data
      stream.pipe(process.stdout)
    }
  }
})()

Read me

List of companies in the S&P 500 (Standard and Poor’s 500). The S&P 500 is a free-float, capitalization-weighted index of the top 500 publicly listed stocks in the US (top 500 by market cap). The dataset includes a list of all the stocks contained therein.

Data

Information on S&P 500 index used to be available on the official webpage on the Standard and Poor’s website but until they publish it back, Wikipedia is the best up-to-date and open data source.

Sources

Detailed information on the S&P 500 (primarily in xls format) used to be obtained from its official webpage on the Standard and Poor’s website - it was free but registration was required.

  • Index listing - see <data/constituents.csv>
    • used to be extracted from source Excel file on S&P website but this no longer contains a list of constituents. (Note this Excel was actually S&P 500 EPS estimates but on sheet 4 it used to have a list of members - previous file was just members but that 404s as of Dec 2014) (Note: but note you have to register and login to access - no longer true as of August 2013)
  • Historical performance (source xls on S&P website)

Note: for aggregate information on the S&P (dividends, earnings etc) see Standard and Poor’s 500 Dataset

General Financial Notes

Publicly listed US companies are obliged various reports on a regular basis with the SEC. Of these 2 types are of especial interest to investors and others interested in their finances and business. These are:

  • 10-K = Annual Report
  • 10-Q = Quarterly report

Preparation

You can run the script yourself to update the data and publish them to github : see scripts README

License

All data is licensed under the Open Data Commons Public Domain Dedication and License. All code is licensed under the MIT/BSD license.

Note that while no credit is formally required a link back or credit to Rufus Pollock and the Open Knowledge Foundation is much appreciated.


Keywords and keyphrases: s&p 500 companies, list of s&p 500 companies, list of companies, standard and poor's 500, s&p 500 list, s and p, constituents.
Datapackage.json

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