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CO2 PPM - Trends in Atmospheric Carbon Dioxide

core

Files Size Format Created Updated License Source
2 2MB csv zip 9 months ago 48 minutes ago
This dataset was created using data from Earth System Research Laboratory. This data contains Atmospheric Carbon Dioxide Dry Air Mole Fractions from quasi-continuous daily measurements at Mauna Loa, Hawaii. Preparation To generate output file you should only run the read more
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Data Files

Download files in this dataset

File Description Size Last changed Download
co2-ppm-daily 358kB csv (358kB) , json (793kB)
co2-ppm-daily_zip Compressed versions of dataset. Includes normalized CSV and JSON data with original data and datapackage.json. 264kB zip (264kB)

co2-ppm-daily  

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

Field information

Field Name Order Type (Format) Description
date 1 string (default)
value 2 string (default)

Integrate this dataset into your favourite tool

Use our data-cli tool designed for data wranglers:

data get https://datahub.io/core/co2-ppm-daily
data info core/co2-ppm-daily
tree core/co2-ppm-daily
# Get a list of dataset's resources
curl -L -s https://datahub.io/core/co2-ppm-daily/datapackage.json | grep path

# Get resources

curl -L https://datahub.io/core/co2-ppm-daily/r/0.csv

curl -L https://datahub.io/core/co2-ppm-daily/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/co2-ppm-daily/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/co2-ppm-daily/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/co2-ppm-daily/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/co2-ppm-daily/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

This dataset was created using data from Earth System Research Laboratory. This data contains Atmospheric Carbon Dioxide Dry Air Mole Fractions from quasi-continuous daily measurements at Mauna Loa, Hawaii.

Preparation

To generate output file you should only run the script: co2-ppm-daily-flow.py

Data

Data has been scraped from two different sources:

  • First source contains values from 01.01.1958 to 31.12.2004
  • Second source contains values from 01.01.1973 to 31.12.2017.
  • Third source contains values until today.

Since there is a lot of overlapping between the sources, output file is created in a way that all existing values are included.

Output file is located in: data/co2-ppm-daily.csv

Value represents Mole fraction reported in units of micromol mol-1 (10-6 mol per mol of dry air); equivalent to ppm (parts per million).

License

ODC-PDDL-1.0

This Data Package is made available under the Public Domain Dedication and License v1.0 whose full text can be found at: http://www.opendatacommons.org/licenses/pddl/1.0/

Notes

The terms of use of the source dataset list three specific restrictions on public use of these data:

The information on government servers are in the public domain, unless specifically annotated otherwise, and may be used freely by the public so long as you do not 1) claim it is your own (e.g. by claiming copyright for NOAA information – see next paragraph), 2) use it in a manner that implies an endorsement or affiliation with NOAA, or 3) modify it in content and then present it as official government material.*

Datapackage.json

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