datahub | A Generalized Metadata Search & Discovery Tool

by linkedin Java Version: v0.8.28 License: Apache-2.0

Download this library from

kandi X-RAY | datahub Summary

datahub is a Java library typically used in Big Data, Kafka, Spark applications. datahub has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has medium support. You can install using 'pip install datahub' or download it from GitHub, PyPI.

DataHub is an open-source metadata platform for the modern data stack. Read about the architectures of different metadata systems and why DataHub excels here. Also read our LinkedIn Engineering blog post, check out our Strata presentation and watch our Crunch Conference Talk. You should also visit DataHub Architecture to get a better understanding of how DataHub is implemented and DataHub Onboarding Guide to understand how to extend DataHub for your own use cases.

Support

Quality

Security

License

Reuse

kandi-support Support

datahub has a medium active ecosystem.
It has 4881 star(s) with 1332 fork(s). There are 221 watchers for this library.
There were 4 major release(s) in the last 6 months.
There are 141 open issues and 824 have been closed. On average issues are closed in 9 days. There are 37 open pull requests and 0 closed requests.
It has a neutral sentiment in the developer community.
The latest version of datahub is v0.8.28

datahub Support

Best in #Java

Average in #Java

datahub Support

Best in #Java

Average in #Java

quality kandi Quality

datahub has no bugs reported.

datahub Quality

Best in #Java

Average in #Java

datahub Quality

Best in #Java

Average in #Java

security Security

datahub has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

datahub Security

Best in #Java

Average in #Java

datahub Security

Best in #Java

Average in #Java

license License

datahub is licensed under the Apache-2.0 License. This license is Permissive.
Permissive licenses have the least restrictions, and you can use them in most projects.

datahub License

Best in #Java

Average in #Java

datahub License

Best in #Java

Average in #Java

build Reuse

datahub releases are available to install and integrate.
Deployable package is available in PyPI.
Build file is available. You can build the component from source.
Installation instructions are available. Examples and code snippets are not available.

datahub Reuse

Best in #Java

Average in #Java

datahub Reuse

Best in #Java

Average in #Java

Top functions reviewed by kandi - BETA

Coming Soon for all Libraries!

Currently covering the most popular Java, JavaScript and Python libraries. See a SAMPLE HERE.
kandi's functional review helps you automatically verify the functionalities of the libraries and avoid rework.

datahub Key Features

Check out DataHub's Features & Roadmap.

datahub Examples and Code Snippets

See all related Code Snippets

Convert JSON to a newline-delimited list of all the things tagged "name" with jq

Copy

Download

$ jq -r '.[].name' world_cities.json
Newport Beach
Nipomo
Norco
North Glendale
North Highlands

How to append data in single cell while writing data of multiple for loop in a csv through Pandas?

Copy

Download

pd.DataFrame(zip(c12,d12),columns=['Date', 'Date_time'])

import pandas as pd

c12 = ['03 Nov 2021', '08 Nov 2021','09 Nov 2021','10 Nov 2021','11 Nov 2021']
d12 = ['18:39','12:59','13:05','12:57','12:57']

pd.DataFrame(zip(c12,d12),columns=['Date', 'Date_time'])

c12 = []
d12 = []

for i in range(d, 0, -1):
    driver.find_element_by_link_text(f'{i}').click()
    time.sleep(5)

    date1 = driver.find_elements_by_class_name('GridBiMonthlyBonusEligibleListDatetd.NoSortColumn')
    for i in reversed(date1):
        print(i.text)
        c12.append(i.text)
        if i is None:
            break

    date_time = driver.find_elements_by_class_name('GridBiMonthlyBonusEligibleListLoginDatetd.NoSortColumn')
    for i1 in reversed(date_time):
        print(i1.text)
        d12.append(i1.text)
        if i1 is None:
            break

pd.DataFrame(zip(c12,d12),columns=['Date', 'Date_time'])#.to_csv(...)

-----------------------

pd.DataFrame(zip(c12,d12),columns=['Date', 'Date_time'])

import pandas as pd

c12 = ['03 Nov 2021', '08 Nov 2021','09 Nov 2021','10 Nov 2021','11 Nov 2021']
d12 = ['18:39','12:59','13:05','12:57','12:57']

pd.DataFrame(zip(c12,d12),columns=['Date', 'Date_time'])

c12 = []
d12 = []

for i in range(d, 0, -1):
    driver.find_element_by_link_text(f'{i}').click()
    time.sleep(5)

    date1 = driver.find_elements_by_class_name('GridBiMonthlyBonusEligibleListDatetd.NoSortColumn')
    for i in reversed(date1):
        print(i.text)
        c12.append(i.text)
        if i is None:
            break

    date_time = driver.find_elements_by_class_name('GridBiMonthlyBonusEligibleListLoginDatetd.NoSortColumn')
    for i1 in reversed(date_time):
        print(i1.text)
        d12.append(i1.text)
        if i1 is None:
            break

pd.DataFrame(zip(c12,d12),columns=['Date', 'Date_time'])#.to_csv(...)

-----------------------

pd.DataFrame(zip(c12,d12),columns=['Date', 'Date_time'])

import pandas as pd

c12 = ['03 Nov 2021', '08 Nov 2021','09 Nov 2021','10 Nov 2021','11 Nov 2021']
d12 = ['18:39','12:59','13:05','12:57','12:57']

pd.DataFrame(zip(c12,d12),columns=['Date', 'Date_time'])

c12 = []
d12 = []

for i in range(d, 0, -1):
    driver.find_element_by_link_text(f'{i}').click()
    time.sleep(5)

    date1 = driver.find_elements_by_class_name('GridBiMonthlyBonusEligibleListDatetd.NoSortColumn')
    for i in reversed(date1):
        print(i.text)
        c12.append(i.text)
        if i is None:
            break

    date_time = driver.find_elements_by_class_name('GridBiMonthlyBonusEligibleListLoginDatetd.NoSortColumn')
    for i1 in reversed(date_time):
        print(i1.text)
        d12.append(i1.text)
        if i1 is None:
            break

pd.DataFrame(zip(c12,d12),columns=['Date', 'Date_time'])#.to_csv(...)

Running a custom JavaScript task in the backend Marklogic server

Copy

Download

task getCollections(type: com.marklogic.gradle.task.MarkLogicTask) {
  doLast {
    def client = getAppConfig().newDatabaseClient()
    String request = """
      cts.collections().toArray().join("; ")
    """;

    try {
      String result
      result = client.newServerEval().javascript(request).evalAs(String.class);
      if (result != null) {
        println result
      }
    } finally {
      client.release()
    }
  }
}

How can I make an interactive world map in dash-leaflet?

Copy

Download

import dash_leaflet as dl
from dash import Dash

# An url pointing to the data that you want to show.
url = "https://pkgstore.datahub.io/core/geo-countries/countries/archive/23f420f929e0e09c39d916b8aaa166fb/countries.geojson"
# Create example app.
app = Dash()
app.layout = dl.Map(children=[dl.TileLayer(), dl.GeoJSON(url=url)],
                    style={'width': '100%', 'height': '50vh', 'margin': "auto", "display": "block"})

if __name__ == '__main__':
    app.run_server()

datahub 505 HTTP Version Not Supported

Copy

Download

location /somelocation/ {
      proxy_pass http://localhost:8080/;
      proxy_http_version 1.1;
      proxy_set_header Upgrade $http_upgrade;
      proxy_set_header Connection 'upgrade';
      proxy_set_header Host $host;
      proxy_cache_bypass $http_upgrade;
  }

Why am I unable to import this CSV file?

Copy

Download

import pandas as pd

-----------------------

import pandas as pd

pip install pandas

-----------------------

import pandas as pd

pip install pandas

Converting geojson into sf for a clotopleth map

Copy

Download

library(sf)

json_file <- 'https://datahub.io/core/geo-countries/datapackage.json'
json_data <- jsonlite::fromJSON(json_file)

## The actual geojson is contained here
geojson <- json_data$resources$path[3]

geojson
# [1] "https://pkgstore.datahub.io/core/geo-countries/countries/archive/23f420f929e0e09c39d916b8aaa166fb/countries.geojson"

sf <- geojsonsf::geojson_sf(geojson)

sf <- sf::st_read(geojson)

-----------------------

library(sf)

json_file <- 'https://datahub.io/core/geo-countries/datapackage.json'
json_data <- jsonlite::fromJSON(json_file)

## The actual geojson is contained here
geojson <- json_data$resources$path[3]

geojson
# [1] "https://pkgstore.datahub.io/core/geo-countries/countries/archive/23f420f929e0e09c39d916b8aaa166fb/countries.geojson"

sf <- geojsonsf::geojson_sf(geojson)

sf <- sf::st_read(geojson)

-----------------------

library(sf)

json_file <- 'https://datahub.io/core/geo-countries/datapackage.json'
json_data <- jsonlite::fromJSON(json_file)

## The actual geojson is contained here
geojson <- json_data$resources$path[3]

geojson
# [1] "https://pkgstore.datahub.io/core/geo-countries/countries/archive/23f420f929e0e09c39d916b8aaa166fb/countries.geojson"

sf <- geojsonsf::geojson_sf(geojson)

sf <- sf::st_read(geojson)

Get country name from ISO code in Javascript

Copy

Download

var getCountryNames = new Intl.DisplayNames(['en'], {type: 'region'});
console.log(getCountryNames.of('AL'));  // "Albania"

How do I search and replace using a loop data in 2 data frame?

Copy

Download

readURL = "https://storage.googleapis.com/covid19-open-data/v2/vaccinations.csv" 
Frame = read.csv(readURL)


readCountryURL = "https://pkgstore.datahub.io/core/country-list/data_csv/data/d7c9d7cfb42cb69f4422dec222dbbaa8/data_csv.csv"
country <- read.csv(readCountryURL, na.strings = '')


library(stringr)
Frame$country <- str_replace_all(str_remove(Frame$key, '_.*'), 
                                 setNames(country$Name, country$Code))

How to send a "string array" as HTTP post data from Python?

Copy

Download

postData = {'meteringPointIds': "1234"}

-----------------------

postData = {
    "meteringPoints": {
        "meteringPoint": [
            "1234"
        ]
    }
}

postData = {
    "meteringPointIds": {
        "meteringPoints": {
            "meteringPoint": [
                "1234"
            ]
        }
    }
}

-----------------------

postData = {
    "meteringPoints": {
        "meteringPoint": [
            "1234"
        ]
    }
}

postData = {
    "meteringPointIds": {
        "meteringPoints": {
            "meteringPoint": [
                "1234"
            ]
        }
    }
}

See all related Code Snippets

Community Discussions

Trending Discussions on datahub

Convert JSON to a newline-delimited list of all the things tagged "name" with jq
How to append data in single cell while writing data of multiple for loop in a csv through Pandas?
Running a custom JavaScript task in the backend Marklogic server
How to assign multiple inputs and outputs to app.callback with hover_feature or click_feature in dash-leaflet?
How can I make an interactive world map in dash-leaflet?
Mongodb: ignore large documents ( BSON > 16 MB) during collection.aggregate()
datahub 505 HTTP Version Not Supported
Why am I unable to import this CSV file?
Converting geojson into sf for a clotopleth map
Get country name from ISO code in Javascript

Trending Discussions on datahub

QUESTION

Convert JSON to a newline-delimited list of all the things tagged "name" with jq

Asked 2022-Mar-17 at 21:00

I'm trying to make a .txt list of cities from a dataset of cities with more than 15000 people. The JSON is structured like this:

[
  {
    "country": "United States",
    "geonameid": 5376890,
    "name": "Newport Beach",
    "subcountry": "California"
  },
  {
    "country": "United States",
    "geonameid": 5377100,
    "name": "Nipomo",
    "subcountry": "California"
  },
  {
    "country": "United States",
    "geonameid": 5377199,
    "name": "Norco",
    "subcountry": "California"
  },
  {
    "country": "United States",
    "geonameid": 5377613,
    "name": "North Glendale",
    "subcountry": "California"
  },
  {
    "country": "United States",
    "geonameid": 5377640,
    "name": "North Highlands",
    "subcountry": "California"
  }
]

I want to take this and make it into a newline-delimited list of all the things tagged "name" like this:

Newport Beach
Nipomo
Norco
North Glendale
North Highlands

I tried to do this on the command line using a tool I found called jq, which I though would work by writing something like

cat world_cities.json | jq '.name' > cities_list.txt

but I got an error that said "jq: error (at :0): Cannot index array with string "name"". Of course, I'm sure I'm not fully understanding how jq is supposed to work, and I don't have very much experience parsing JSON, but I'm having trouble finding an answer to my specific problem online. Does anyone know what I can potentially do to achieve what I'm trying to achieve from the command line?

If you want to see the whole dataset I'm trying to parse, you can find it here: https://pkgstore.datahub.io/core/world-cities/world-cities_json/data/5b3dd46ad10990bca47b04b4739a02ba/world-cities_json.json

ANSWER

Answered 2022-Mar-17 at 21:00

$ jq -r '.[].name' world_cities.json
Newport Beach
Nipomo
Norco
North Glendale
North Highlands

The data is surrounded by an array. You can access the elements with .[]: . is the array itself and [] gives its elements. Then .name gives you each element's name.

Finally, use -r to have jq output raw strings without quotes.

Source https://stackoverflow.com/questions/71518883

QUESTION

How to append data in single cell while writing data of multiple for loop in a csv through Pandas?

Asked 2022-Mar-14 at 19:10

Problem:-

I've two nested loop inside a main for loop fetching different data from a range of 5 pages. Every page has 5 rows, But after writing data to csv, I only get 5 cells in which each cell has 5 values.

Solution I want :-

I want all the dates as well as time to be in different rows, so total there should be at least 25 rows.

My Minimal Code :-

all = []

for i in range(d, 0, -1):
    driver.find_element_by_link_text(f'{i}').click()
    time.sleep(5)
    c12 = []
    d12 = []

    date1 = driver.find_elements_by_class_name('Login_d')
    for i in reversed(date1):
        print(i.text)
        c12.append(i.text)
        if i is None:
            break

    date_time = driver.find_elements_by_class_name('Logout_d')
    for i1 in reversed(date_time):
        print(i1.text)
        d12.append(i1.text)
        if i1 is None:
            break

    z = [c12, d12]
    all.append(z)


 df = pd.DataFrame(all)
 path = 'C:\\Application Data\\pyt_project\\datahub'
 a1 = ['Date', 'Date_time']
 filename = 
 dt.datetime.now().strftime("Login_logout_time_%d_%b_%y_%I_%M_%p.csv")
 p1 = os.path.join(path, filename)
 df.to_csv(p1, headers = a1, index=True)
 print('printed output')

CSV Output I got from above code

                              Date                                        Date_time
 03 Nov 2021 08 Nov 2021 09 Nov 2021 10 Nov 2021 11 Nov 2021 | 18:39 12:59 13:05 12:57 12:57

CSV Output I want

   Date     Date_time
03 Nov 2021  18:39
08 Nov 2021  12:59
09 Nov 2021  13:05
10 Nov 2021  12:57
11 Nov 2021  12:57

Things I tried but didn't worked

df = pd.DataFrame({'Date' : [c12],
                'Date_time' : [d12]
                },
                columns=['Date', 'Date_time'])

ANSWER

Answered 2021-Dec-10 at 13:28

Note Because the question only considers part of the code, I'll assume that everything that happens before works and only go into the obvious parts

I want all the dates as well as time to be in different rows

You can simply zip() your two lists if they have the same length:

pd.DataFrame(zip(c12,d12),columns=['Date', 'Date_time'])

Example

import pandas as pd

c12 = ['03 Nov 2021', '08 Nov 2021','09 Nov 2021','10 Nov 2021','11 Nov 2021']
d12 = ['18:39','12:59','13:05','12:57','12:57']

pd.DataFrame(zip(c12,d12),columns=['Date', 'Date_time'])

Output

Date	Date_time
03 Nov 2021	18:39
08 Nov 2021	12:59
09 Nov 2021	13:05
10 Nov 2021	12:57
11 Nov 2021	12:57

But after writing data to csv, I only get 5 cells in which each cell has 5 values.

Put the lists to keep your data outside the loop and check your indentation.

c12 = []
d12 = []

for i in range(d, 0, -1):
    driver.find_element_by_link_text(f'{i}').click()
    time.sleep(5)

    date1 = driver.find_elements_by_class_name('GridBiMonthlyBonusEligibleListDatetd.NoSortColumn')
    for i in reversed(date1):
        print(i.text)
        c12.append(i.text)
        if i is None:
            break

    date_time = driver.find_elements_by_class_name('GridBiMonthlyBonusEligibleListLoginDatetd.NoSortColumn')
    for i1 in reversed(date_time):
        print(i1.text)
        d12.append(i1.text)
        if i1 is None:
            break

pd.DataFrame(zip(c12,d12),columns=['Date', 'Date_time'])#.to_csv(...)

Source https://stackoverflow.com/questions/70304900

QUESTION

How to assign multiple inputs and outputs to app.callback with hover_feature or click_feature in dash-leaflet?

Asked 2021-Nov-20 at 17:56

I'm having trouble getting multiple inputs and outputs to work in Dash-leaflet. Essentially, I want to do the following: on a mouse click place a marker on the map, and when hovering over a country highlight an area of the map with a polygon and display some information about the country being hovered over in a different pane. I implemented each of those functions in a separate app.callback, as in the below code.

app = dash.Dash(__name__)

app.layout = html.Div(children=[
    dl.Map(children=[dl.TileLayer(), 
                     
                     dl.LayerGroup(id="layer"),
                     
                     
                     dl.GeoJSON(url='https://pkgstore.datahub.io/core/geo-countries/countries/archive/23f420f929e0e09c39d916b8aaa166fb/countries.geojson', id="countries",
                                options=dict(style=dict(opacity=0,fillOpacity=0)),
                                hoverStyle=arrow_function(dict(weight=2, color='red', dashArray='', opacity=1)))
                     ],


            style={'width': '1000px', 'height': '500px'}, zoom=3, id='map'),
    
    html.Div(id='country'),
    
    html.Div(id='Country info pane')

])

@app.callback(Output("layer", "children"), [Input("countries", "click_feature")])
def map_click(feature):

    return [dl.Marker(position=[np.random.randint(-90,90),np.random.randint(-185,185)])]


@app.callback(Output('Country info pane', 'children'),
              Input('countries', 'hover_feature'))
def country_hover(feature):
    if feature is not None:
        country_id = feature['properties']['ISO_A3']

        return country_id

@app.callback(Output('layer', 'children'),
              Input('countries', 'hover_feature'))
def country_hover(feature):
    if feature is not None:
        return(dl.Polygon(positions=[[30,40],[50,60]], color='#ff002d', opacity=0.2))
      


if __name__ == '__main__':
    app.run_server(debug=False)

While each of those callbacks works in isolation (i.e. if I comment out the other callbacks), none of them work if I run the code with two or more of them active.

If I combine the two hover_feature functions like the below, they seem to work together.

@app.callback(Output('Country info pane', 'children'),
              Output('layer', 'children'),
              Input('countries', 'hover_feature'))
def country_hover(feature):
    if feature is not None:
        country_id = feature['properties']['ISO_A3']

        return country_id, dl.Polygon(positions=[[30,40],[50,60]], color='#ff002d', opacity=0.2)

However, the "map_click" function still does not work along with the "country_hover" function. How can I get all three callbacks to work?

ANSWER

Answered 2021-Nov-20 at 17:56

I found out how to do this, I had to add another dl.LayerGroup to the dl.Map object with a different id, then assigned each function to output to a different layer.

Source https://stackoverflow.com/questions/69966166

QUESTION

Converting geojson into sf for a clotopleth map

Asked 2021-Aug-19 at 23:52

I have recently been wrapping my head around the leaflet package and have gotten around the basics of adding markers, reading/plotting shapefiles from local source and displaying the final outputs in Shiny.

I am currently trying to understand a bit more how to access geojson files directly from the web since the maps that I create in Shiny aren't being properly displayed when uploaded to shinyapps.io (I think it's because the local files aren't being uploaded with the app itself, need to read a bit more on that)

That being said I found this link which contains the political map of all countries in the world. The added documentation suggest using the following code to obtain the polygons but the suggested method does not seem to be working for me.

library(jsonlite)
json_file <- 'https://datahub.io/core/geo-countries/datapackage.json'
json_data <- fromJSON(paste(readLines(json_file), collapse=""))

I tried using geojson_sf(json_data) to convert the extracted information into an sf but I get the error "Error in UseMethod". From what I have read it seems that the best format to work with spatial information is sf which would allow me to left_join the desired information and add population data to each country by its name.

Can I get a pointer on how to extract the polygons so I can addPolygons() them into my leaflet map?

Example data:

Countries <- structure(list(Canada = 37590000, UnitedStates = 328200000, Mexico = 127600000), row.names = c(NA, -1L), class = c("tbl_df", "tbl", "data.frame"))

Appreciate any pointers or links to references which can clarify my question. Thanks!

ANSWER

Answered 2021-Aug-19 at 23:52

A couple of points in your question suggest a misunderstanding of what geojson is, and what the functions you're using are supposed to do:

geojson_sf() is designed to work on raw json/geojson. So it won't work on your already-parsed json_data.
You say you're trying to work with geojson files, but the link you give is not geojson

If you go to the link https://datahub.io/core/geo-countries/datapackage.json you'll see it's JSON describing the data, and it gives a path of the actual geojson file

library(sf)

json_file <- 'https://datahub.io/core/geo-countries/datapackage.json'
json_data <- jsonlite::fromJSON(json_file)

## The actual geojson is contained here
geojson <- json_data$resources$path[3]

geojson
# [1] "https://pkgstore.datahub.io/core/geo-countries/countries/archive/23f420f929e0e09c39d916b8aaa166fb/countries.geojson"

So now you know where the geojson is located, you can read it in a couple of different ways:

Using geojsonsf

sf <- geojsonsf::geojson_sf(geojson)

Using sf

sf <- sf::st_read(geojson)

You can / should use sf in this instance because the data is not large and it can easily handle it.

I built geojsonsf for when you need a bit more speed, and for it to handle the cases where the sf geojson reader struggles on large files

Source https://stackoverflow.com/questions/68847071

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install datahub

Please follow the DataHub Quickstart Guide to get a copy of DataHub up & running locally using Docker. As the guide assumes some basic knowledge of Docker, we'd recommend you to go through the "Hello World" example of A Docker Tutorial for Beginners if Docker is completely foreign to you.