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#%%writefile csv_to_dict.py
# Your task is to read the input DATAFILE line by line, and for the first 10 lines (not including the header)
# split each line on "," and then for each line, create a dictionary
# where the key is the header title of the field, and the value is the value of that field in the row.
# The function parse_file should return a list of dictionaries,
# each data line in the file being a single list entry.
# Field names and values should not contain extra whitespace, like spaces or newline characters.
# You can use the Python string method strip() to remove the extra whitespace.
# You have to parse only the first 10 data lines in this exercise,
# so the returned list should have 10 entries!
import os
import pprint
DATADIR = ""
DATAFILE = "beatles-diskography.csv"
def parse_file(datafile):
with open(datafile, "r") as f:
items = [line.strip().split(',') for line in f]
key_list = items[0]
data = [dict(zip(key_list,items[i])) for i in range(1,11)]
return data
def test():
# a simple test of your implemetation
datafile = os.path.join(DATADIR, DATAFILE)
d = parse_file(datafile)
pprint.pprint(d)
firstline = {'Title': 'Please Please Me', 'UK Chart Position': '1', 'Label': 'Parlophone(UK)', 'Released': '22 March 1963', 'US Chart Position': '-', 'RIAA Certification': 'Platinum', 'BPI Certification': 'Gold'}
tenthline = {'Title': '', 'UK Chart Position': '1', 'Label': 'Parlophone(UK)', 'Released': '10 July 1964', 'US Chart Position': '-', 'RIAA Certification': '', 'BPI Certification': 'Gold'}
assert d[0] == firstline
assert d[9] == tenthline
test()
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# %%writefile parse_excel.py
import xlrd
datafile = "2013_ERCOT_Hourly_Load_Data.xls"
import pprint
def parse_file(datafile):
workbook = xlrd.open_workbook(datafile)
sheet = workbook.sheet_by_index(0)
data = [[sheet.cell_value(r, col)
for col in range(sheet.ncols)]
for r in range(sheet.nrows)]
print "\nList Comprehension"
print "data[3][2]:",
print data[3][2]
print "\nCells in a nested loop:"
for row in range(sheet.nrows):
for col in range(sheet.ncols):
if row == 50:
print sheet.cell_value(row, col),
### other useful methods:
print "\nROWS, COLUMNS, and CELLS:"
print "Number of rows in the sheet:",
print sheet.nrows
print "Type of data in cell (row 3, col 2):",
print sheet.cell_type(3, 2)
print "Value in cell (row 3, col 2):",
print sheet.cell_value(3, 2)
print "Get a slice of values in column 3, from rows 1-3:"
print sheet.col_values(3, start_rowx=1, end_rowx=4)
print "\nDATES:"
print "Type of data in cell (row 1, col 0):",
print sheet.cell_type(1, 0)
exceltime = sheet.cell_value(1, 0)
print "Time in Excel format:",
print exceltime
print "Convert time to a Python datetime tuple, from the Excel float:",
print xlrd.xldate_as_tuple(exceltime, 0)
#pprint.pprint(data[1:10])
task = """
Your task is as follows:
- read the provided Excel file
- find and return the min, max and average values for the COAST region
- find and return the time value for the min and max entries
- the time values should be returned as Python tuples
Please see the test function for the expected return format
"""
list_coast = sheet.col_values(1,start_rowx=1)
max_val = max(list_coast)
min_val = min(list_coast)
d_data = {
'maxtime': xlrd.xldate_as_tuple(data[list_coast.index(max_val)+1][0],0),
'maxvalue':max_val,
'mintime': xlrd.xldate_as_tuple(data[list_coast.index(min_val)+1][0],0),
'minvalue': min_val,
'avgcoast': sum(list_coast)/len(list_coast)
}
pprint.pprint(d_data)
return data
if __name__ == "__main__":
data = parse_file(datafile)
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# %%writefile ws_json_music_brain.py
# To experiment with this code freely you will have to run this code locally.
# We have provided an example json output here for you to look at,
# but you will not be able to run any queries through our UI.
import json
import requests
import pprint
BASE_URL = "http://musicbrainz.org/ws/2/"
ARTIST_URL = BASE_URL + "artist/"
query_type = { "simple": {},
"atr": {"inc": "aliases+tags+ratings"},
"aliases": {"inc": "aliases"},
"releases": {"inc": "releases"}}
def query_site(url, params, uid="", fmt="json"):
params["fmt"] = fmt
r = requests.get(url + uid, params=params)
print "requesting", r.url
if r.status_code == requests.codes.ok:
return r.json()
else:
r.raise_for_status()
def query_by_name(url, params, name):
params["query"] = "artist:" + name
return query_site(url, params)
def pretty_print(data, indent=4):
if type(data) == dict:
print json.dumps(data, indent=indent, sort_keys=True)
else:
print data
def main():
results = query_by_name(ARTIST_URL, query_type["simple"], "The Beatles")
aliases = results["artists"][0]["aliases"]
for a in aliases:
if a["locale"] == "es":
pretty_print(a["name"])
# pretty_print(results['artists'][0]['aliases'])
# print(len(results['artists']))
# for e in results['artists']:
# try:
# pprint.pprint(e['aliases'])
# except KeyError:
# continue
# try:
# [e['aliases'] for e in results['artists']]
# except KeyError:
# continue
artist_id = results["artists"][1]["id"]
# print len(results['artists'][1])
# print "\nARTIST:"
# pretty_print(results["artist"][1])
artist_data = query_site(ARTIST_URL, query_type["aliases"], artist_id)
pretty_print(artist_data,indent=10)
# releases = artist_data["aliases"]
# pretty_print(artist_data)
# print "\nONE RELEASE:"
# pretty_print(releases[0], indent=2)
# release_titles = [r["title"] for r in releases]
# print "\nALL TITLES:"
# for t in release_titles:
# print t
# q1 = query_by_name(ARTIST_URL, query_type["simple"], "FIRST AID KIT")
# q1_count = 0
# for artist in q1["artists"]:
# if artist["score"] == "100":
# q1_count += 1
# print "Q1: Number of bands 'FIRST AID KIT' that had score == 100"
# print q1_count
if __name__ == '__main__':
main()
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%%writefile problem11.py
#!/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
import pandas
DATADIR = ""
DATAFILE = "745090.csv"
def parse_file(datafile):
name = ""
data = []
with open(datafile,'rb') as f:
items = list(csv.reader(f))
name = items[0][1]
data = items[2:]
# Do not change the line below
return (name, data)
def parse_file2(datafile):
name = ""
data = []
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)
#print name
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()
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%%writefile problem12.py
# -*- 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
from zipfile import ZipFile
import pandas
datafile = "2013_ERCOT_Hourly_Load_Data.xls"
outfile = "2013_Max_Loads.csv"
def open_zip(datafile):
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 = []
# YOUR CODE HERE
# Remember that you can use xlrd.xldate_as_tuple(sometime, 0) to convert
# Excel date to Python tuple of (year, month, day, hour, minute, second)
xldata = [[str(sheet.cell_value(r,c)) for c in range(sheet.ncols)] for r in range(sheet.nrows)]
headers = ["Station",'Year','Month','Day','Hour','Max Load']
data.append(headers)
for i in range(1,9):
list_reg = sheet.col_values(i,start_rowx=1)
max_val = max(list_reg)
exceltime = xldata[list_reg.index(max_val)+1][0]
#print exceltime
max_time = xlrd.xldate_as_tuple(float(exceltime),0)
y,m,d,h,v1,v2 = max_time
data.append([str(xldata[0][i]),str(y),str(m),str(d),str(h),repr(max_val)])
return data
def save_file(data, filename):
# YOUR CODE HERE
with open(filename, 'wb') as f:
writercsv = csv.writer(f,delimiter='|')
for row in data:
writercsv.writerow(row)
def test():
#open_zip(datafile)
data = parse_file(datafile)
#print data
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:
#print ans[station][field]
#print line[field]
assert ans[station][field] == line[field]
number_of_rows += 1
stations.append(station)
# Check Station Names
assert set(stations) == set(correct_stations)
# Output should be 8 lines not including header
assert number_of_rows == 8
print "All tests passed"
test()
if __name__ == "__main__":
test()
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%%writefile problem13.py
#!/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
URL_MAIN = "http://api.nytimes.com/svc/"
URL_POPULAR = URL_MAIN + "mostpopular/v2/"
API_KEY = { "popular": "",
"article": ""}
def get_from_file(kind, period):
filename = "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)
# YOUR CODE HERE
titles = [{e['section']:e['title']} for e in data]
urls=[]
for js1 in data:
for js2 in js1['media']:
for js3 in js2['media-metadata']:
if js3['format'] == "Standard Thumbnail":
urls.append(js3['url'])
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 = URL_POPULAR + "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}-full.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()
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