Let’s say you have these two tables:
name | type | age | height |
---|---|---|---|
Kant | Cat | 2 | 50 |
Hegel | Cat | 2 | 10 |
Descartes | Cat | 5 | 30 |
Marx | Dog | 2 | 50 |
import requests # dependency | |
url = "<your url>" # webhook url, from here: https://i.imgur.com/f9XnAew.png | |
# for all params, see https://discordapp.com/developers/docs/resources/webhook#execute-webhook | |
data = { | |
"content" : "message content", | |
"username" : "custom username" | |
} |
#!/usr/bin/env python | |
import os | |
import urllib.parse | |
from flask import Flask | |
from flask_sqlalchemy import SQLAlchemy | |
# Configure Database URI: | |
params = urllib.parse.quote_plus("DRIVER={SQL Server};SERVER=sqlhost.database.windows.net;DATABASE=pythonSQL;UID=username@sqldb;PWD=password56789") |
/* | |
* script to export data of the named sheet as an individual csv files | |
* sheet downloaded to Google Drive and then downloaded as a CSV file | |
* file named according to the name of the sheet | |
* original author: Michael Derazon (https://gist.github.com/mderazon/9655893) | |
*/ | |
function onOpen() { | |
var ss = SpreadsheetApp.getActiveSpreadsheet(); | |
var csvMenuEntries = [{name: "Download Primary Time File", functionName: "saveAsCSV"}]; |
Anaconda is an excellent, simple way to get Python up and running on your computer. But, it includes a lot of packages you'll never use but consume gigs and gigs of hard drive space. Instead, you can just install miniconda and then choose the individual packages you need. The steps below explain how to do this to set up a Python environment for geospatial data science. These steps are Windows-specific, but the same process works on Mac or Linux (just don't download the wheels from Gohlke - conda/pip install them directly). If you're having trouble, here are more detailed instructions on getting geopandas and geospatial Python up and running.
C:\Anaconda
and set it as the system's default Python# 10_basic.py | |
# 15_make_soup.py | |
# 20_search.py | |
# 25_navigation.py | |
# 30_edit.py | |
# 40_encoding.py | |
# 50_parse_only_part.py |
YOUR_API_KEY --> https://developers.google.com/maps/documentation/geocoding/get-api-key
grel:"https://maps.googleapis.com/maps/api/geocode/json?address=" + escape(value,"url") + "&key=YOUR_API_KEY"
donde value podria ser: "Balcarce 50, CABA, Argentina" o "Avellaneda, provincia de Buenos Aires, Argentina"
// Lefalet shortcuts for common tile providers - is it worth adding such 1.5kb to Leaflet core? | |
L.TileLayer.Common = L.TileLayer.extend({ | |
initialize: function (options) { | |
L.TileLayer.prototype.initialize.call(this, this.url, options); | |
} | |
}); | |
(function () { | |
for /R %f in (*.shp) do ogr2ogr -f "GeoJSON" "%~dpnf.geojson" "%f" -t_srs EPSG:4326 |