Employee Salaries Montgomery County MD 2014 to 2016

JohnSnowLabs

Files Size Format Created Updated License Source
2 21MB csv zip 3 months ago John Snow Labs Standard License John Snow Labs Data.gov
Download

Data Files

File Description Size Last changed Download
employee-salaries-montgomery-county-md-2014-to-2016-csv 5MB csv (5MB) , json (12MB)
employee-salaries-montgomery-county-md-2014-to-2016_zip Compressed versions of dataset. Includes normalized CSV and JSON data with original data and datapackage.json. 3MB zip (3MB)

employee-salaries-montgomery-county-md-2014-to-2016-csv  

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

Field information

Field Name Order Type (Format) Description
Year 1 date (%Y-%m-%d) Data Year
Full_Name 2 string Name of employee
Gender 3 string Gender of the employee
Current_Annual_Salary_Dollar 4 number Salary in dollars
Gross_Pay_Received_Dollar 5 number Gross pay received by the employee
Overtime_Pay_Dollar 6 number Overtime pay received by the employee
Department 7 string Department code in which the employee is working
Department_Name 8 string Name of the department in which the employee is working
Division 9 string Division of the dept in which the employee is working
Assignment_Category 10 string Work category, Full time or part time
Position_Title 11 string Job title
Underfilled_Job_Title 12 string Underfill job title
Date_First_Hired 13 date (%Y-%m-%d) Date of hiring

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 datahub.io almost like you use git with the github. Here are installation instructions.

data get https://datahub.io/JohnSnowLabs/employee-salaries-montgomery-county-md-2014-to-2016
tree JohnSnowLabs/employee-salaries-montgomery-county-md-2014-to-2016
# Get a list of dataset's resources
curl -L -s https://datahub.io/JohnSnowLabs/employee-salaries-montgomery-county-md-2014-to-2016/datapackage.json | grep path

# Get resources

curl -L https://datahub.io/JohnSnowLabs/employee-salaries-montgomery-county-md-2014-to-2016/r/0.csv

curl -L https://datahub.io/JohnSnowLabs/employee-salaries-montgomery-county-md-2014-to-2016/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/JohnSnowLabs/employee-salaries-montgomery-county-md-2014-to-2016/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/JohnSnowLabs/employee-salaries-montgomery-county-md-2014-to-2016/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/JohnSnowLabs/employee-salaries-montgomery-county-md-2014-to-2016/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/JohnSnowLabs/employee-salaries-montgomery-county-md-2014-to-2016/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