sample-2sheets-average-wasp-63

anuveyatsu

Files Size Format Created Updated License Source
4 0B xlsx zip 6 months ago
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Data Files

File Description Size Last changed Download
sample-2sheets 5kB xlsx (5kB)
sample-2sheets-sheet-1 5kB xlsx (5kB)
sample-2sheets-sheet-2 5kB xlsx (5kB)
datapackage_zip Compressed versions of dataset. Includes normalized CSV and JSON data with original data and datapackage.json. 14kB zip (14kB)

sample-2sheets  

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

sample-2sheets-sheet-1  

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

sample-2sheets-sheet-2  

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

Import into your tool

Data-cli or just data is the program to get and post your data with the datahub.
Download CLI tool and use it with the datahub almost like you use git with the github:

data get https://datahub.io/anuveyatsu/sample-2sheets-average-wasp-63
data info anuveyatsu/sample-2sheets-average-wasp-63
tree anuveyatsu/sample-2sheets-average-wasp-63
# Get a list of dataset's resources
curl -L -s https://datahub.io/anuveyatsu/sample-2sheets-average-wasp-63/datapackage.json | grep path

# Get resources

curl -L https://datahub.io/anuveyatsu/sample-2sheets-average-wasp-63/r/0.xlsx

curl -L https://datahub.io/anuveyatsu/sample-2sheets-average-wasp-63/r/1.xlsx

curl -L https://datahub.io/anuveyatsu/sample-2sheets-average-wasp-63/r/2.xlsx

curl -L https://datahub.io/anuveyatsu/sample-2sheets-average-wasp-63/r/3.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/anuveyatsu/sample-2sheets-average-wasp-63/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/anuveyatsu/sample-2sheets-average-wasp-63/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/anuveyatsu/sample-2sheets-average-wasp-63/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/anuveyatsu/sample-2sheets-average-wasp-63/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)
    }
  }
})()
Datapackage.json