#!/usr/bin/env python
"""
Your task is to process the supplied file and use the csv module to extract data from it.
The data comes from NREL (National Renewable Energy Laboratory) website. Each file
contains information from one meteorological station, in particular - about amount of
solar and wind energy for each hour of day.
Note that the first line of the datafile is neither data entry, nor header. It is a line
describing the data source. You should extract the name of the station from it.
The data should be returned as a list of lists (not dictionaries).
You can use the csv modules "reader" method to get data in such format.
Another useful method is next() - to get the next line from the iterator.
You should only change the parse_file function.
"""
import csv
import os
DATADIR = "./data/"
DATAFILE = "745090.csv"
def parse_file(datafile):
name = None
header = None
data = []
with open(datafile,'rb') as f:
r = csv.reader(f)
for line in r:
# get the name
if not name:
name = line[1]
continue
# get the header
if not header:
header = line
continue
# add the list for the current line to `data`
data.append(line)
return (name, data)
# from the instructor
def parse_file(datafile):
with open(datafile,'rb') as f:
r = csv.reader(f)
name = r.next()[1]
header = r.next()
data = [row for row in r]
return (name, data)
def test():
datafile = os.path.join(DATADIR, DATAFILE)
name, data = parse_file(datafile)
assert name == "MOUNTAIN VIEW MOFFETT FLD NAS"
assert data[0][1] == "01:00"
assert data[2][0] == "01/01/2005"
assert data[2][5] == "2"
if __name__ == "__main__":
test()
# -*- coding: utf-8 -*-
# Find the time and value of max load for each of the regions
# COAST, EAST, FAR_WEST, NORTH, NORTH_C, SOUTHERN, SOUTH_C, WEST
# and write the result out in a csv file, using pipe character | as the delimiter.
# An example output can be seen in the "example.csv" file.
import xlrd
import os
import csv
import numpy as np
from zipfile import ZipFile
datafile = "./data/2013_ERCOT_Hourly_Load_Data.xls"
outfile = "./data/2013_Max_Loads.csv"
def open_zip(datafile):
pass
#with ZipFile('{0}.zip'.format(datafile), 'r') as myzip:
#myzip.extractall()
def parse_file(datafile):
workbook = xlrd.open_workbook(datafile)
sheet = workbook.sheet_by_index(0)
data = [['Station','Year','Month','Day','Hour','Max Load']]
for i in range(1,sheet.ncols-1):
max_ind = np.argmax(sheet.col_values(i, start_rowx=1, end_rowx=None))
max_val = sheet.cell_value(max_ind+1,i)
(year,month,day,hour,_,_) = xlrd.xldate_as_tuple(sheet.cell_value(max_ind+1,0), 0)
station = sheet.cell_value(0,i)
data.append([station, year, month, day, hour, max_val])
return data
def save_file(data, filename):
#csv.register_dialect('verticalbar', delimiter='|')
with open(filename, 'wb') as f:
writer = csv.writer(f, delimiter='|')
writer.writerows(data)
return None
def test():
open_zip(datafile)
data = parse_file(datafile)
save_file(data, outfile)
number_of_rows = 0
stations = []
ans = {'FAR_WEST': {'Max Load': '2281.2722140000024',
'Year': '2013',
'Month': '6',
'Day': '26',
'Hour': '17'}}
correct_stations = ['COAST', 'EAST', 'FAR_WEST', 'NORTH',
'NORTH_C', 'SOUTHERN', 'SOUTH_C', 'WEST']
fields = ['Year', 'Month', 'Day', 'Hour', 'Max Load']
with open(outfile) as of:
csvfile = csv.DictReader(of, delimiter="|")
for line in csvfile:
station = line['Station']
if station == 'FAR_WEST':
for field in fields:
# Check if 'Max Load' is within .1 of answer
if field == 'Max Load':
max_answer = round(float(ans[station][field]), 1)
max_line = round(float(line[field]), 1)
assert max_answer == max_line
# Otherwise check for equality
else:
assert ans[station][field] == line[field]
number_of_rows += 1
stations.append(station)
# Output should be 8 lines not including header
assert number_of_rows == 8
# Check Station Names
assert set(stations) == set(correct_stations)
if __name__ == "__main__":
test()
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
This exercise shows some important concepts that you should be aware about:
- using codecs module to write unicode files
- using authentication with web APIs
- using offset when accessing web APIs
To run this code locally you have to register at the NYTimes developer site
and get your own API key. You will be able to complete this exercise in our UI without doing so,
as we have provided a sample result.
Your task is to process the saved file that represents the most popular (by view count)
articles in the last day, and return the following data:
- list of dictionaries, where the dictionary key is "section" and value is "title"
- list of URLs for all media entries with "format": "Standard Thumbnail"
All your changes should be in the article_overview function.
The rest of functions are provided for your convenience, if you want to access the API by yourself.
"""
import json
import codecs
import requests
import pprint
pp = pprint.PrettyPrinter(indent=2)
URL_MAIN = "http://api.nytimes.com/svc/"
URL_POPULAR = URL_MAIN + "mostpopular/v2/"
API_KEY = { "popular": "",
"article": ""}
def get_from_file(kind, period):
filename = "./data/popular-{0}-{1}.json".format(kind, period)
with open(filename, "r") as f:
return json.loads(f.read())
def article_overview(kind, period):
data = get_from_file(kind, period)
titles = [{x['section']:x['title']} for x in data]
urls = [z['url'] for x in data for y in x['media'] for z in y['media-metadata'] if z['format']=='Standard Thumbnail']
return (titles, urls)
def query_site(url, target, offset):
# This will set up the query with the API key and offset
# Web services often use offset paramter to return data in small chunks
# NYTimes returns 20 articles per request, if you want the next 20
# You have to provide the offset parameter
if API_KEY["popular"] == "" or API_KEY["article"] == "":
print "You need to register for NYTimes Developer account to run this program."
print "See Intructor notes for information"
return False
params = {"api-key": API_KEY[target], "offset": offset}
r = requests.get(url, params = params)
if r.status_code == requests.codes.ok:
return r.json()
else:
r.raise_for_status()
def get_popular(url, kind, days, section="all-sections", offset=0):
# This function will construct the query according to the requirements of the site
# and return the data, or print an error message if called incorrectly
if days not in [1,7,30]:
print "Time period can be 1,7, 30 days only"
return False
if kind not in ["viewed", "shared", "emailed"]:
print "kind can be only one of viewed/shared/emailed"
return False
url += "most{0}/{1}/{2}.json".format(kind, section, days)
data = query_site(url, "popular", offset)
return data
def save_file(kind, period):
# This will process all results, by calling the API repeatedly with supplied offset value,
# combine the data and then write all results in a file.
data = get_popular(URL_POPULAR, "viewed", 1)
num_results = data["num_results"]
full_data = []
with codecs.open("popular-{0}-{1}.json".format(kind, period), encoding='utf-8', mode='w') as v:
for offset in range(0, num_results, 20):
data = get_popular(URL_POPULAR, kind, period, offset=offset)
full_data += data["results"]
v.write(json.dumps(full_data, indent=2))
def test():
titles, urls = article_overview("viewed", 1)
assert len(titles) == 20
assert len(urls) == 30
assert titles[2] == {'Opinion': 'Professors, We Need You!'}
assert urls[20] == 'http://graphics8.nytimes.com/images/2014/02/17/sports/ICEDANCE/ICEDANCE-thumbStandard.jpg'
if __name__ == "__main__":
test()
#titles, urls = article_overview("viewed", 1)