Support
Quality
Security
License
Reuse
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.
Check out DataHub's Features & Roadmap.
Convert JSON to a newline-delimited list of all the things tagged "name" with jq
$ 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?
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
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?
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
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 $http_upgrade;
}
Why am I unable to import this CSV file?
import pandas as pd
-----------------------
import pandas as pd
pip install pandas
-----------------------
import pandas as pd
pip install pandas
Converting geojson into sf for a clotopleth map
library(sf)
json_file <- 'https://datahub.io/core/geo-countries/datapackage.json'
json_data <- jsonlite::fromJSON(json_file)
## The actual geojson is contained here
geojson <- json_data$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$path[3]
geojson
# [1] "https://pkgstore.datahub.io/core/geo-countries/countries/archive/23f420f929e0e09c39d916b8aaa166fb/countries.geojson"
sf <- geojsonsf::geojson_sf(geojson)
sf <- sf::st_read(geojson)
Get country name from ISO code in Javascript
var getCountryNames = new Intl.DisplayNames(['en'], {type: 'region'});
console.log(getCountryNames.of('AL')); // "Albania"
How do I search and replace using a loop data in 2 data frame?
readURL = "https://storage.googleapis.com/covid19-open-data/v2/vaccinations.csv"
Frame = read.csv(readURL)
readCountryURL = "https://pkgstore.datahub.io/core/country-list/data_csv/data/d7c9d7cfb42cb69f4422dec222dbbaa8/data_csv.csv"
country <- read.csv(readCountryURL, na.strings = '')
library(stringr)
Frame$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?
postData = {'meteringPointIds': "1234"}
-----------------------
postData = {
"meteringPoints": {
"meteringPoint": [
"1234"
]
}
}
postData = {
"meteringPointIds": {
"meteringPoints": {
"meteringPoint": [
"1234"
]
}
}
}
-----------------------
postData = {
"meteringPoints": {
"meteringPoint": [
"1234"
]
}
}
postData = {
"meteringPointIds": {
"meteringPoints": {
"meteringPoint": [
"1234"
]
}
}
}
QUESTION
Convert JSON to a newline-delimited list of all the things tagged "name" with jq
Asked 2022-Mar-17 at 21:00I'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.
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
No vulnerabilities reported
Save this library and start creating your kit
Save this library and start creating your kit