United States Air Carrier Fatal Accidents By First Phase Of Operation

JohnSnowLabs

Files Size Format Created Updated License Source
2 45kB csv zip 3 months ago John Snow Labs Standard License John Snow Labs Bureau of Transportation Statistics
Download

Data Files

File Description Size Last changed Download
united-states-air-carrier-fatal-accidents-by-first-phase-of-operation-csv 916B csv (916B) , json (8kB)
united-states-air-carrier-fatal-accidents-by-first-phase-of-operation_zip Compressed versions of dataset. Includes normalized CSV and JSON data with original data and datapackage.json. 4kB zip (4kB)

united-states-air-carrier-fatal-accidents-by-first-phase-of-operation-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) Year of data
Total_Fatal_Accidents 2 integer Total number of fatal accidents happened in the particular year of US Air carrrier by first phase of operation. Carriers operating under 14 CFR 121. Before Mar. 20, 1997, 14 CFR 121 applied only to aircraft with more than 30 seats or a maximum payload capacity of more than 7,500 pounds. Since Mar. 20, 1997, 14 CFR 121 includes aircraft with 10 or more seats that formerly operated under 14 CFR 135. This change makes it difficult to compare pre-1997 data with more recent data.
Phase_Of_Operation_Approach_Or_Descent_Or_Landing 3 integer Number of fatal accidents during the phase of operation. First phase of operation is the phase of flight in which the first occurrence leading to the accident happened.
Phase_Of_Operation_Taxi_Or_Takeoff_Or_Climb 4 integer Number of fatal accidents during the phase of operation. First phase of operation is the phase of flight in which the first occurrence leading to the accident happened.
Phase_Of_Operation_Cruise_In_Flight 5 integer Number of fatal accidents during the phase of operation. First phase of operation is the phase of flight in which the first occurrence leading to the accident happened.c Cruise (in-flight) numbers for 2001 are unusually high because of the incidents occurring on September 11, 2001.
Phase_Of_Operation_Standing_Static 6 integer Number of fatal accidents during the phase of operation. First phase of operation is the phase of flight in which the first occurrence leading to the accident happened.
Phase_Of_Operation_Maneuvering 7 integer Number of fatal accidents during the phase of operation. First phase of operation is the phase of flight in which the first occurrence leading to the accident happened.
Other_Fatalities_Happened_Or_Not_Reported 8 integer Other reasons for fatalities

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/united-states-air-carrier-fatal-accidents-by-first-phase-of-operation
tree JohnSnowLabs/united-states-air-carrier-fatal-accidents-by-first-phase-of-operation
# Get a list of dataset's resources
curl -L -s https://datahub.io/JohnSnowLabs/united-states-air-carrier-fatal-accidents-by-first-phase-of-operation/datapackage.json | grep path

# Get resources

curl -L https://datahub.io/JohnSnowLabs/united-states-air-carrier-fatal-accidents-by-first-phase-of-operation/r/0.csv

curl -L https://datahub.io/JohnSnowLabs/united-states-air-carrier-fatal-accidents-by-first-phase-of-operation/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/united-states-air-carrier-fatal-accidents-by-first-phase-of-operation/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/united-states-air-carrier-fatal-accidents-by-first-phase-of-operation/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/united-states-air-carrier-fatal-accidents-by-first-phase-of-operation/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/united-states-air-carrier-fatal-accidents-by-first-phase-of-operation/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