sergiolucero

from pptx import Presentation
from pptx.util import Inches
import glob

SLIDE_LAYOUT_TITLE_AND_CONTENT = 1
FILTER = 'fotos/*.jpg'     # what to include

prs = Presentation()
slide_layout = prs.slide_layouts[SLIDE_LAYOUT_TITLE_AND_CONTENT]

sergiolucero

from creds import ID,CODE       # isolines: driving from my house

import folium
import requests

home =[-33.406654,-70.572701]     # 085 LOS MILITARES / ALONSO DE CORDOVA
head = 'https://isoline.route.cit.api.here.com/routing/7.2/calculateisoline.json?'
URL_BASE = '{}app_id={}&app_code={}&mode=shortest;car;traffic:disabled&start=geo!{},{}&range={}&rangetype={}'

def isodata(home, range, type):

sergiolucero

import aiohttp
import asyncio
import time, pandas as pd

def async_http_get(urls, extractor=None, json_response=True):
    tasks = []
    sem = asyncio.Semaphore(32)

    async def fetch(session, url):
        async with session.get(url) as response:

jflasher

Currently, only data within the last 90 days is available via the OpenAQ API. However, there is much more data available on OpenAQ and a variety of different access mechanisms. Note also that there is work under way to bring back to the API a mechanism to access the data older than 90 days, details here.

If you're looking to query across all the data or even easily export the data (or a subset of it), the easiest way to do that currently is using a service like Amazon Athena. I'll provide some directions on how to do that below, but at a high level, this will let you make any query of the entire dataset that you'd like (written in SQL). I'll also provide some sample queries so you can see what's possible.

On to the directions!

1. You will need to create an AWS account if you don't currently have one, you can start this process at htt

sergiolucero

import sqlite3
import pandas as pd
db = sqlite3.connect('database.db')
tables = pd.read_sql_query("SELECT * FROM sqlite_master WHERE type='table'", db) 
print(tables)

nygeog

import pandas as pd
from geopandas import GeoDataFrame
from shapely.geometry import Point
import fiona

df = pd.read_csv('data.csv')

geometry = [Point(xy) for xy in zip(df.x, df.y)]
crs = {'init': 'epsg:2263'} #http://www.spatialreference.org/ref/epsg/2263/
geo_df = GeoDataFrame(df, crs=crs, geometry=geometry)

sergiolucero

import glob, os, pptx, scipy.misc
from PIL import Image

size = 800,600
prs = pptx.Presentation()

for fn in glob.glob('*.jpg'):
	img = Image.open(fn)
	img.thumbnail(size, Image.ANTIALIAS)
	img.save(fn)

eskerda

# -*- coding: utf-8 -*-
"""
    ❯ termbike.py "36 Maple St, NY"
    396 - Lefferts Pl & Franklin Ave
    🚲 🚲 🚲 🚲 🚲 🚲 🚲 🚲 🚲 🚲 🚲 🚲 🚲 🚲  ☮ ☮ ☮ ☮ ☮ ☮ ☮ ☮ ☮ ☮ ☮
    365 - Fulton St & Grand Ave
    🚲 🚲 🚲 🚲 🚲  ☮ ☮ ☮ ☮ ☮ ☮ ☮ ☮ ☮ ☮ ☮ ☮ ☮ ☮ ☮ ☮ ☮ ☮ ☮ ☮ ☮ ☮ ☮ ☮ ☮ ☮
    436 - Hancock St & Bedford Ave
    🚲 🚲 🚲 🚲 🚲  ☮ ☮ ☮ ☮ ☮ ☮ ☮ ☮ ☮ ☮ ☮ ☮ ☮ ☮ ☮ ☮ ☮ ☮ ☮ ☮ ☮ ☮
    437 - Macon St & Nostrand Ave

kinnala

import urllib2
import json
import re

class SoccerwayTeamMatches:

    def __init__(self, teamId):
        self.teamId = str(teamId)
        self.data = {'all': [], 'home': [], 'away': []}