convert float string to numpy array with different rows [duplicate] - python

This question already has answers here:
Efficient way to convert delimiter separated string to numpy array
(3 answers)
Closed 8 years ago.
I have string final_line as
0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0130631958731,0.0,0.0,0.0,0.0,0.0,0.0,0.0130631958731,0.0,0.0,0.0,0.0,0.0,0.0,,
0.00507937313707,0.0,0.0,0.0201058520009,0.0,0.0,0.0459562331449,0.0268078026679,0.0,0.0103772139359,0.0,0.0,0.0438673134565,0.0,0.0268078026679,0.0,0.0,0.0,0.0,0.0,0.0224437417685,,
0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.070802847389,0.0,0.0,,
0.0,0.0,0.169140135429,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,,
0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0261263917462,0.0,0.0,0.0,0.0,0.0,0.0,0.0261263917462,0.0,0.0,0.0,0.0,0.0,0.0,,
0.0961428138228,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,,
I want to convert it into numpy array for without transferring to CSV file. Here each line in string should be converted to different row as
[['0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0.08364751'], ['0.017944717', '0', '0', '0', '0.009470823', '0', '0', '0', '0', '0', '0'], ['0.012620501', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0'], ['0.012620501', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0'], ['0', '0', '0', '0', '0.01332164', '0', '0', '0', '0', '0', '0'], ['0.012620501', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0'], ['0', '0', '0', '0', '0', '0.097414177', '0.042092545', '0', '0', '0', '0'], ['0', '0', '0', '0', '0.01332164', '0', '0', '0', '0', '0', '0'], ['0.005324215', '0', '0', '0.04598186', '0.028100025', '0', '0', '0', '0', '0', '0.023525603'], ['0', '0', '0', '0', '0', '0', '0', '0', '0.055765006', '0', '0'], ['0', '0', '0.133216404', '0', '0', '0', '0', '0', '0', '0', '0']]
I know here output having different values but I have just shown to get the format
Example
I have string as
a1,b1,c1,d1,e1,,
a2,b2,c2,d2,e2,,
a3,b3,c3,d3,e3,,
then output should be
[['a1','b1','c1','d1','e1'],
['a2','b2','c2','d2','e2'],
['a3','b3','c3','d3','e3']]
I have seen other questions but it converts into only single row of array. Here I want every single line in new row as shown in example.


map can be useful in this case. Also, the last ,, in your final list is not necessary so I just ignored it (look at :-2 before split operation).
import numpy as np
A = final_line[:-2].split(',,')
B = np.array([map(float,a.split(',')) for a in A])


Your data is in the form of lists, with each line separated by an additional comma.
d_in = "1,2,3,,4,5,6,,7,8,9"
first = d_in.split(",,")
final = [line.split(",") for line in first]
first stores a list of strings (each of your columns). final stores your data in the proper format (in my example, [[1,2,3],[4,5,6],[7,8,9]])

Related

How can you sort a list of lists by the number of nonzero elements in each sublist?

I have a list of lists (8 lists of 15 elements each) and it's like below:
mylist = [
['Adam', '0', '1', '0', '1', '0', '1', '0', '0', '1', '0', '1', '0', '1', '0'],
['Bobby', '0', '0', '0', '0', '1', '1', '0', '0', '0', '0', '0', '1', '1', '0'],
['Felicia', '0', '0', '1', '0', '1', '0', '0', '0', '0', '1', '0', '1', '0', '0'],
['Jake', '0', '1', '1', '1', '1', '1', '0', '0', '0', '0', '0', '0', '0', '0'],
['MikeP', '0', '0', '1', '0', '1', '0', '0', '0', '1', '1', '0', '1', '0', '0'],
['MikeF', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0'],
['Shannon', '0', '1', '0', '0', '1', '0', '0', '0', '0', '1', '0', '1', '0', '0'],
['Tom', '0', '1', '1', '1', '1', '0', '0', '0', '1', '1', '1', '1', '0', '0']
]
Using Python 3, I'm trying to get mylist sorted beginning with the most zeros in a sublist to the fewest zeros in a sublist. I don't want to sort within each list - the 0s and 1s need to stay where they are ultimately.
I have tried using len() and lambda functions and other ideas I find here like breaking it down with the following:
for index1, value1 in enumerate(mylist):
mylistsorted.append([value2 for index2, value2 in enumerate(mylist[index1]) if value2 != '0'])
mylistsorted.sort(key = len)
But I lose all the '0's of course in that new sorted list. Should this perhaps be done with something else like numpy or matrices or something else vice lists of lists? Thank you for any help...

Use list.count as your key for sorted to count all the 0's in each list:
sorted(mylist, key=lambda x: -x[1:].count('0'))

Use a key that counts the number of 0's:
mylist.sort( key = lambda l : -l.count('0') )

Taking a not-so-wild guess and counting the ones instead:
mylist.sort(key=lambda l: l.count('1'))

How to iterate through a bunch of values in a dictionaries that are lists python

ratinglist = open("ratings.txt").readlines()
booklist = open("booklist.txt").readlines()
keys = [key.strip('\n') for key in ratinglist[0::2]]
values = [value.strip(' \n').split(' ') for value in ratinglist[1::2]]
my_dict = dict(zip(keys, values))
for values in my_dict:
value * x = sum
So, I have a giant dictionary that has a list of values for each key.
Here is an example of my dictionary:
'KeeLed': ['0', '0', '0', '5', '0', '0', '0', '5', '0', '5', '5', '0', '0', '0', '0', '0', '5', '-3', '-3', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '1', '0', '0', '0', '0', '5', '0', '0', '5', '0', '5', '5', '5', '5', '0', '0', '0', '0', '0', '3', '1', '0', '0', '0', '0'], 'Megan': ['5', '5', '0', '0', '0', '0', '0', '0', '0', '3', '0', '5', '0', '0', '1', '0', '5', '0', '1', '5', '0', '0', '0', '0', '0', '1', '0', '5', '0', '0', '3', '5', '5', '0', '0', '5', '0', '0', '3', '0', '0', '3', '5', '5', '0', '0', '0', '0', '0', '5', '5', '0', '5', '0', '0']}
What I need to do is do a for loop, that loops through each value, multiplies them, and then adds them.
I just need help getting the for loop to iterate through the values correctly.
For example, for KeeLed and Megan, I want this for loop to iterate through their seperate lists of values, and multiply them, then add them.
Like this: 0 * 5, + 5 * 0, + 0 * 0, + 5 * 0.... etc etc
The issue is I haven't found anything online on how to iterate through lists that are inside a dictionary . I've found some resources on looping through lists, but I need to loop through lists of values that are inside of a dictionary.
I would like the results to be put in a list, but I could do that if I get some help with the for loop.
scores for each person = [325, 450]

"The issue is I haven't found anything online on how to iterate through lists that are inside a dictionary . I've found some resources on looping through lists, but I need to loop through lists of values that are inside of a dictionary."
EXAMPLE
myDict = {
'KeeLed': ['0', '0', '0', '5', '0', '0', '0', '5', '0', '5', '5', '0', '0', '0', '0', '0', '5', '-3', '-3', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '1', '0', '0', '0', '0', '5', '0', '0', '5', '0', '5', '5', '5', '5', '0', '0', '0', '0', '0', '3', '1', '0', '0', '0', '0'],
'Megan': ['5', '5', '0', '0', '0', '0', '0', '0', '0', '3', '0', '5', '0', '0', '1', '0', '5', '0', '1', '5', '0', '0', '0', '0', '0', '1', '0', '5', '0', '0', '3', '5', '5', '0', '0', '5', '0', '0', '3', '0', '0', '3', '5', '5', '0', '0', '0', '0', '0', '5', '5', '0', '5', '0', '0']
}
# For-looping through dict will give the key.
for name in myDict:
total = 0
# applying the key into myDict will give the list associated with the dict
for score in myDict[name]:
total = total + int(score)
print(name,total)
Result
KeeLed 54
Megan 85

How to put data in a csv file, with python?

I want to put lists of data in a csv file, this is my data
list_info = [('1552150125', '02141592cc00000001', '2', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '1', '0000000200', '0', '0', '0', '23'), ('1552150125', '02141592cc00000001', '5', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '1', '0000000500', '0', '0', '0', '23'), ('1552150125', '02141592cc00000001', '6', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '1', '0000000600', '0', '0', '0', '23'), ('1552150125', '02141592cc00000001', '7', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '1', '0000000700', '0', '0', '0', '23'), ('1552150125', '02141592cc00000001', '8', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '1', '0000000800', '0', '0', '0', '23'), ('1552150125', '02141592cc00000001', '9', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '1', '0000000900', '0', '0', '0', '23'), ('1552150125', '02141592cc00000001', '10', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '1', '0000000a00', '0', '0', '0', '23'), ('1552150125', '02141592cc00000001', '11', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '1', '0000000b00', '0', '0', '0', '23'), ('1552150125', '02141592cc00000001', '12', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '1', '0000000c00', '0', '0', '0', '23'), ('1552150125', '02141592cc00000001', '13', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '1', '0000000d00', '0', '0', '0', '23'), ('1552150125', '02141592cc00000001', '14', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '1', '0000000e00', '0', '0', '0', '23'), ('1552150125', '02141592cc00000001', '15', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '1', '0000000f00', '0', '0', '0', '23')]
This is my code:
print(list_info)
for row in list_info:
print (row)
for column in row:
out.write(column)
out.write('\n')
out.close()
But it does not work, it gives me every list concatenated in the same column, like this:
155215012502141592cc000000012000000000001000000020000023
The expected result is:
time | ID |a|b|c|d|e|f|g|h|i|j|k|l|m|n |o|p|q|r|
1552150|02141592cc00000001|2|0|0|0|0|0|0|0|0|0|0|0|1|0000000200|0|0|0|23|

You could just use the csv module,
import csv
# your list_info here
header = 'time ID a b c d e f g h i j k l m n o p q r'.split()
with open('some_data.csv', 'w') as csvfile:
writer = csv.writer(csvfile, delimiter='|')
writer.writerow(header)
writer.writerows(list_info) # write all the rows at once as suggested by #roganjosh

use csv package:
import csv
list_info = [('1552150125', '02141592cc00000001', '2', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '1', '0000000200', '0', '0', '0', '23'), ('1552150125', '02141592cc00000001', '5', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '1', '0000000500', '0', '0', '0', '23'), ('1552150125', '02141592cc00000001', '6', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '1', '0000000600', '0', '0', '0', '23'), ('1552150125', '02141592cc00000001', '7', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '1', '0000000700', '0', '0', '0', '23'), ('1552150125', '02141592cc00000001', '8', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '1', '0000000800', '0', '0', '0', '23'), ('1552150125', '02141592cc00000001', '9', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '1', '0000000900', '0', '0', '0', '23'), ('1552150125', '02141592cc00000001', '10', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '1', '0000000a00', '0', '0', '0', '23'), ('1552150125', '02141592cc00000001', '11', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '1', '0000000b00', '0', '0', '0', '23'), ('1552150125', '02141592cc00000001', '12', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '1', '0000000c00', '0', '0', '0', '23'), ('1552150125', '02141592cc00000001', '13', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '1', '0000000d00', '0', '0', '0', '23'), ('1552150125', '02141592cc00000001', '14', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '1', '0000000e00', '0', '0', '0', '23'), ('1552150125', '02141592cc00000001', '15', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '1', '0000000f00', '0', '0', '0', '23')]
h = 'time ID a b c d e f g h i j k l m n o p q r'.split()
with open('file.csv', 'w',newline='') as myfile:
wr = csv.writer(myfile, quoting=csv.QUOTE_NONE)
wr.writerow(h)
wr.writerows(list_info)

A solution with elementary Python tools.
Using csv module of the standard library would be also a solid solution. This just uses str.format. Note, that I advise to always use an encoding when opening files.
out = open('test.csv', 'w', encoding='utf-8')
for row in list_info:
out.write('|'.join(row))
out.write('\n')
out.close()

Convert list of string into comma separated 2D NumPy

I have a dataset like the following:
0000000000000001,0
0000000000000010,0
0000000000000011,1
0000000000000100,1
0000000000000101,1
0000000000000110,0
0000000000000111,1
0000000000001000,0
0000000000001001,0
.
.
.
I want to convert the first column from each row to be a comma seperated something like this:
From 0000000000000001,0 to 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0
The following code has a small issue: full_data is not 2D numpy of integers, yet it is 1D of string. I am not sure how to fix that. Any hint is appreciated.
data = []
my_data = np.genfromtxt('file.csv', delimiter=',', dtype='str')[:,0]
for i in my_data:
x= np.asarray(i)
data.append(x.tolist())
full_data = np.asarray(data)
Thank you

This is one approach.
Demo:
import numpy as np
data = []
my_data = np.genfromtxt(filename, delimiter=',', dtype='str')
data = [list(i) + [j] for i, j in my_data]
print(data)
Output:
[
['0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '1', '0'],
['0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '1', '0', '0'],
['0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '1', '1', '1'],
['0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '1', '0', '0', '1'],
['0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '1', '0', '1', '1'],
['0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '1', '1', '0', '0'],
['0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '1', '1', '1', '1'],
['0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '1', '0', '0', '0', '0'],
['0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '1', '0', '0', '1', '0']
]

XML Parsing to .txt file Python

I need to parse this XML Document and move the date and time into a %Y-%m-%d %H:%M:%S format as well as the variables hourly-qpf and probability-of-precipitation to columns in a tab-delimited .txt file.
All I have managed to do is read in the XML file using this code:
page = urllib2.urlopen('http://forecast.weather.gov/MapClick.php?lat=47.6062&lon=-122.3321&FcstType=digitalDWML')
page_content = page.read()
with open('KBFI.xml', 'w') as fid:
fid.write(page_content)
I am at a loss after this. I've only parsed one XML doc before, and it looked completely different from this.
EDIT
Sorry for not having anything to give you guys before, but I wasn't sure what module to use, as I only have experience with minidom and it didn't seem like the right choice. I've been messing around with Element Tree and I have come up with this:
data = []
import xml.etree.ElementTree as ET
tree = ET.parse('KBFI.xml')
root = tree.getroot()
for data in root.findall('data'):
for time-layout in root.findall('time-layout'):
start-valid-time = time-layout.find('start-valid-time')
time = datetime.datetime.strptime(start-valid-time, '%Y-%m-%dT%H:%M:%S')
for parameters in root.findall('parameters'):
for probability-of-precipitation in root.findall('probability-of-precipitation'):
value = probability-of-precipitation.find('value')
for hourly-qpf in root.findall('hourly-qpf'):
value2 = hourly-qpf.find('value')
data = data.append([time,
value,
value2])
with open('KBFI.txt','w') as file:
file.writelines('\t'.join(map(str,i)) + '\n' for i in data)
However, there is a problem because the variables are hyphenated and I do not know how to change them to underscores or remove them. Also, because of this, I have no idea if my code is any good!

You can use the python xml lib:
https://docs.python.org/2/library/xml.etree.elementtree.html
import urllib2
import xml.etree.ElementTree as ET
page = urllib2.urlopen('http://forecast.weather.gov/MapClick.php?lat=47.6062&lon=-122.3321&FcstType=digitalDWML')
page_content = page.read()
root = ET.fromstring(page_content)
for _f in root.itertext():
***Do your formatting here***

I suggest using xmltodict for parsing and extracting data from XML because it is straightforward and easy to use since it converts XML to Python dicts with the same nesting as the XML source. For those familiar with Python syntax, using it is natural and Python dicts are fully versatile, meaning they are capable of expressing heterogeneous and nested data stuctures. For example the Pickling Tools Library relies on Python dicts for Python, C++ and Java data interoperability and provides tools for converting XML to dict. Advantages of xmltodict are that its small, fast, and a standalone module just for converting XML to dict.
As an example of xmltodict usage, the following script downloads this XML document and extracts its creation-date and lists of probability-of-precipitation and hourly-qpf values:
import requests
url='http://forecast.weather.gov/MapClick.php?lat=47.6062&lon=-122.3321&FcstType=digitalDWML'
r = requests.get(url)
import xmltodict
result = xmltodict.parse(r.text)
cd = result['dwml']['head']['product']['creation-date']['#text']
print("creation-date =",cd)
pop = result['dwml']['data']['parameters']['probability-of-precipitation']['value']
print("\nprobability-of-precipitation =", pop)
hqpf = result['dwml']['data']['parameters']['hourly-qpf']['value']
print("\nhourly-qpf =", hqpf)
Here is the output from running this script (on 20150730):
creation-date = 2015-07-30T08:53:12-07:00
probability-of-precipitation = ['0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '7', '7', '7', '7', '7', '7', '7', '7', '7', '7', '7', '7', '5', '5', '5', '5', '5', '5', '5', '5', '5', '5', '5', '5', '8', '8', '8', '8', '8', '8', '8', '8', '8', '8', '8', '8', '6', '6', '6', '6', '6', '6', '6', '6', '6', '6', '6', '6', '13', '13', '13', '13', '13', '13', '13', '13', '13', '13', '13', '13', '19', '19', '19', '19', '19', '19', '19', '19', '19', '19', '19', '19', '28', '28', '28', '28', '28', '28']
hourly-qpf = ['0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0.0067', '0.0067', '0.0067', '0.0067', '0.0067', '0.0067', '0.0033', '0.0033', '0.0033', '0.0033', '0.0033', '0.0033', '0.0067', '0.0067', '0.0067', '0.0067', '0.0067', '0.0067']
xmltodict can be installed with 'pip install xmltodict'. It was developed by Martin Blech and its GitHub project is at https://github.com/martinblech/xmltodict.
In order to access start-valid-time and end-valid-time its helps to know their data structures as well their locations. Since both are a series of values enclosed in identical tags, intuitively each series should be formed in a separate list as the value of a key with their name similar to probability-of-precipitation and hourly-qpf. This can be confirmed by printing the entire result dict and inspecting the format of start-valid-time and end-valid-time in it and that can be facilitated by pretty printing the result dict (with import pprint and then running pprint.pprint(result)). For this XML document, pretty printing its equivilant dict generates over 2000 lines, however start-valid-time begins on line 26 and its value is clearly an list:
{'dwml': {'#version': '1.0',
'#xmlns:xsd': 'http://www.w3.org/2001/XMLSchema',
'#xmlns:xsi': 'http://www.w3.org/2001/XMLSchema-instance',
'#xsi:noNamespaceSchemaLocation': 'http://graphical.weather.gov/xml/DWMLgen/schema/DWML.xsd',
'head': {'product': {'#concise-name': 'tabular-digital',
'#operational-mode': 'developmental',
'#srsName': 'WGS 1984',
'creation-date': {'#refresh-frequency': 'PT1H',
'#text': '2015-07-31T14:20:30-07:00'}},
'source': {'production-center': 'Seattle, WA',
'credit': 'http://www.wrh.noaa.gov/sew',
'more-information': 'http://www.nws.noaa.gov/forecasts/xml/'}},
'data': {'location': {'location-key': 'point1',
'description': 'Downtown Seattle WA, WA',
'point': {'#latitude': '47.61',
'#longitude': '-122.32'},
'city': {'#state': 'WA',
'#text': 'Downtown Seattle WA'},
'height': {'#datum': 'mean sea level',
'#text': '240'}},
'moreWeatherInformation': {'#applicable-location': 'point1',
'#text': 'http://forecast.weather.gov/MapClick.php?lat=47.61&lon=-122.32&FcstType=digital'},
'time-layout': {'#time-coordinate': 'local',
'#summarization': 'none',
'layout-key': 'k-p1h-n1-0',
'start-valid-time': ['2015-07-31T16:00:00-07:00',
'2015-07-31T17:00:00-07:00',
'2015-07-31T18:00:00-07:00',
...
Here is a script that extracts and prints creation-date as a scalar value, all start-valid-time values in a list, all end-valid-time values in a list, all probability-of-precipitation values in a list and all hourly-qpf values in a list and prints the length of each extracted list:
import xmltodict
result = xmltodict.parse(r.text)
cd = result['dwml']['head']['product']['creation-date']['#text']
print("creation-date =",cd)
svt = result['dwml']['data']['time-layout']['start-valid-time']
print("\nstart-valid-time =", svt)
print("number of start-valid-time entries =", len(svt))
evt = result['dwml']['data']['time-layout']['end-valid-time']
print("\nend-valid-time =", evt)
print("number of end-valid-time entries =", len(evt))
pop = result['dwml']['data']['parameters']['probability-of-precipitation']['value']
print("\nprobability-of-precipitation =", pop)
print("number of probability-of-precipitation entries =", len(pop))
hqpf = result['dwml']['data']['parameters']['hourly-qpf']['value']
print("\nhourly-qpf =", hqpf)
print("number of hourly-qpf entries =", len(hqpf))
Here is the output from running this script (on 20150731):
creation-date = 2015-07-31T14:20:30-07:00
start-valid-time = ['2015-07-31T16:00:00-07:00', '2015-07-31T17:00:00-07:00', '2015-07-31T18:00:00-07:00', '2015-07-31T19:00:00-07:00', '2015-07-31T20:00:00-07:00', '2015-07-31T21:00:00-07:00', '2015-07-31T22:00:00-07:00', '2015-07-31T23:00:00-07:00', '2015-08-01T00:00:00-07:00', '2015-08-01T01:00:00-07:00', '2015-08-01T02:00:00-07:00', '2015-08-01T03:00:00-07:00', '2015-08-01T04:00:00-07:00', '2015-08-01T05:00:00-07:00', '2015-08-01T06:00:00-07:00', '2015-08-01T07:00:00-07:00', '2015-08-01T08:00:00-07:00', '2015-08-01T09:00:00-07:00', '2015-08-01T10:00:00-07:00', '2015-08-01T11:00:00-07:00', '2015-08-01T12:00:00-07:00', '2015-08-01T13:00:00-07:00', '2015-08-01T14:00:00-07:00', '2015-08-01T15:00:00-07:00', '2015-08-01T16:00:00-07:00', '2015-08-01T17:00:00-07:00', '2015-08-01T18:00:00-07:00', '2015-08-01T19:00:00-07:00', '2015-08-01T20:00:00-07:00', '2015-08-01T21:00:00-07:00', '2015-08-01T22:00:00-07:00', '2015-08-01T23:00:00-07:00', '2015-08-02T00:00:00-07:00', '2015-08-02T01:00:00-07:00', '2015-08-02T02:00:00-07:00', '2015-08-02T03:00:00-07:00', '2015-08-02T04:00:00-07:00', '2015-08-02T05:00:00-07:00', '2015-08-02T06:00:00-07:00', '2015-08-02T07:00:00-07:00', '2015-08-02T08:00:00-07:00', '2015-08-02T09:00:00-07:00', '2015-08-02T10:00:00-07:00', '2015-08-02T11:00:00-07:00', '2015-08-02T12:00:00-07:00', '2015-08-02T13:00:00-07:00', '2015-08-02T14:00:00-07:00', '2015-08-02T15:00:00-07:00', '2015-08-02T16:00:00-07:00', '2015-08-02T17:00:00-07:00', '2015-08-02T18:00:00-07:00', '2015-08-02T19:00:00-07:00', '2015-08-02T20:00:00-07:00', '2015-08-02T21:00:00-07:00', '2015-08-02T22:00:00-07:00', '2015-08-02T23:00:00-07:00', '2015-08-03T00:00:00-07:00', '2015-08-03T01:00:00-07:00', '2015-08-03T02:00:00-07:00', '2015-08-03T03:00:00-07:00', '2015-08-03T04:00:00-07:00', '2015-08-03T05:00:00-07:00', '2015-08-03T06:00:00-07:00', '2015-08-03T07:00:00-07:00', '2015-08-03T08:00:00-07:00', '2015-08-03T09:00:00-07:00', '2015-08-03T10:00:00-07:00', '2015-08-03T11:00:00-07:00', '2015-08-03T12:00:00-07:00', '2015-08-03T13:00:00-07:00', '2015-08-03T14:00:00-07:00', '2015-08-03T15:00:00-07:00', '2015-08-03T16:00:00-07:00', '2015-08-03T17:00:00-07:00', '2015-08-03T18:00:00-07:00', '2015-08-03T19:00:00-07:00', '2015-08-03T20:00:00-07:00', '2015-08-03T21:00:00-07:00', '2015-08-03T22:00:00-07:00', '2015-08-03T23:00:00-07:00', '2015-08-04T00:00:00-07:00', '2015-08-04T01:00:00-07:00', '2015-08-04T02:00:00-07:00', '2015-08-04T03:00:00-07:00', '2015-08-04T04:00:00-07:00', '2015-08-04T05:00:00-07:00', '2015-08-04T06:00:00-07:00', '2015-08-04T07:00:00-07:00', '2015-08-04T08:00:00-07:00', '2015-08-04T09:00:00-07:00', '2015-08-04T10:00:00-07:00', '2015-08-04T11:00:00-07:00', '2015-08-04T12:00:00-07:00', '2015-08-04T13:00:00-07:00', '2015-08-04T14:00:00-07:00', '2015-08-04T15:00:00-07:00', '2015-08-04T16:00:00-07:00', '2015-08-04T17:00:00-07:00', '2015-08-04T18:00:00-07:00', '2015-08-04T19:00:00-07:00', '2015-08-04T20:00:00-07:00', '2015-08-04T21:00:00-07:00', '2015-08-04T22:00:00-07:00', '2015-08-04T23:00:00-07:00', '2015-08-05T00:00:00-07:00', '2015-08-05T01:00:00-07:00', '2015-08-05T02:00:00-07:00', '2015-08-05T03:00:00-07:00', '2015-08-05T04:00:00-07:00', '2015-08-05T05:00:00-07:00', '2015-08-05T06:00:00-07:00', '2015-08-05T07:00:00-07:00', '2015-08-05T08:00:00-07:00', '2015-08-05T09:00:00-07:00', '2015-08-05T10:00:00-07:00', '2015-08-05T11:00:00-07:00', '2015-08-05T12:00:00-07:00', '2015-08-05T13:00:00-07:00', '2015-08-05T14:00:00-07:00', '2015-08-05T15:00:00-07:00', '2015-08-05T16:00:00-07:00', '2015-08-05T17:00:00-07:00', '2015-08-05T18:00:00-07:00', '2015-08-05T19:00:00-07:00', '2015-08-05T20:00:00-07:00', '2015-08-05T21:00:00-07:00', '2015-08-05T22:00:00-07:00', '2015-08-05T23:00:00-07:00', '2015-08-06T00:00:00-07:00', '2015-08-06T01:00:00-07:00', '2015-08-06T02:00:00-07:00', '2015-08-06T03:00:00-07:00', '2015-08-06T04:00:00-07:00', '2015-08-06T05:00:00-07:00', '2015-08-06T06:00:00-07:00', '2015-08-06T07:00:00-07:00', '2015-08-06T08:00:00-07:00', '2015-08-06T09:00:00-07:00', '2015-08-06T10:00:00-07:00', '2015-08-06T11:00:00-07:00', '2015-08-06T12:00:00-07:00', '2015-08-06T13:00:00-07:00', '2015-08-06T14:00:00-07:00', '2015-08-06T15:00:00-07:00', '2015-08-06T16:00:00-07:00', '2015-08-06T17:00:00-07:00', '2015-08-06T18:00:00-07:00', '2015-08-06T19:00:00-07:00', '2015-08-06T20:00:00-07:00', '2015-08-06T21:00:00-07:00', '2015-08-06T22:00:00-07:00', '2015-08-06T23:00:00-07:00', '2015-08-07T00:00:00-07:00', '2015-08-07T01:00:00-07:00', '2015-08-07T02:00:00-07:00', '2015-08-07T03:00:00-07:00', '2015-08-07T04:00:00-07:00', '2015-08-07T05:00:00-07:00', '2015-08-07T06:00:00-07:00', '2015-08-07T07:00:00-07:00', '2015-08-07T08:00:00-07:00', '2015-08-07T09:00:00-07:00', '2015-08-07T10:00:00-07:00', '2015-08-07T11:00:00-07:00', '2015-08-07T12:00:00-07:00', '2015-08-07T13:00:00-07:00', '2015-08-07T14:00:00-07:00', '2015-08-07T15:00:00-07:00']
number of start-valid-time entries = 168
end-valid-time = ['2015-07-31T17:00:00-07:00', '2015-07-31T18:00:00-07:00', '2015-07-31T19:00:00-07:00', '2015-07-31T20:00:00-07:00', '2015-07-31T21:00:00-07:00', '2015-07-31T22:00:00-07:00', '2015-07-31T23:00:00-07:00', '2015-08-01T00:00:00-07:00', '2015-08-01T01:00:00-07:00', '2015-08-01T02:00:00-07:00', '2015-08-01T03:00:00-07:00', '2015-08-01T04:00:00-07:00', '2015-08-01T05:00:00-07:00', '2015-08-01T06:00:00-07:00', '2015-08-01T07:00:00-07:00', '2015-08-01T08:00:00-07:00', '2015-08-01T09:00:00-07:00', '2015-08-01T10:00:00-07:00', '2015-08-01T11:00:00-07:00', '2015-08-01T12:00:00-07:00', '2015-08-01T13:00:00-07:00', '2015-08-01T14:00:00-07:00', '2015-08-01T15:00:00-07:00', '2015-08-01T16:00:00-07:00', '2015-08-01T17:00:00-07:00', '2015-08-01T18:00:00-07:00', '2015-08-01T19:00:00-07:00', '2015-08-01T20:00:00-07:00', '2015-08-01T21:00:00-07:00', '2015-08-01T22:00:00-07:00', '2015-08-01T23:00:00-07:00', '2015-08-02T00:00:00-07:00', '2015-08-02T01:00:00-07:00', '2015-08-02T02:00:00-07:00', '2015-08-02T03:00:00-07:00', '2015-08-02T04:00:00-07:00', '2015-08-02T05:00:00-07:00', '2015-08-02T06:00:00-07:00', '2015-08-02T07:00:00-07:00', '2015-08-02T08:00:00-07:00', '2015-08-02T09:00:00-07:00', '2015-08-02T10:00:00-07:00', '2015-08-02T11:00:00-07:00', '2015-08-02T12:00:00-07:00', '2015-08-02T13:00:00-07:00', '2015-08-02T14:00:00-07:00', '2015-08-02T15:00:00-07:00', '2015-08-02T16:00:00-07:00', '2015-08-02T17:00:00-07:00', '2015-08-02T18:00:00-07:00', '2015-08-02T19:00:00-07:00', '2015-08-02T20:00:00-07:00', '2015-08-02T21:00:00-07:00', '2015-08-02T22:00:00-07:00', '2015-08-02T23:00:00-07:00', '2015-08-03T00:00:00-07:00', '2015-08-03T01:00:00-07:00', '2015-08-03T02:00:00-07:00', '2015-08-03T03:00:00-07:00', '2015-08-03T04:00:00-07:00', '2015-08-03T05:00:00-07:00', '2015-08-03T06:00:00-07:00', '2015-08-03T07:00:00-07:00', '2015-08-03T08:00:00-07:00', '2015-08-03T09:00:00-07:00', '2015-08-03T10:00:00-07:00', '2015-08-03T11:00:00-07:00', '2015-08-03T12:00:00-07:00', '2015-08-03T13:00:00-07:00', '2015-08-03T14:00:00-07:00', '2015-08-03T15:00:00-07:00', '2015-08-03T16:00:00-07:00', '2015-08-03T17:00:00-07:00', '2015-08-03T18:00:00-07:00', '2015-08-03T19:00:00-07:00', '2015-08-03T20:00:00-07:00', '2015-08-03T21:00:00-07:00', '2015-08-03T22:00:00-07:00', '2015-08-03T23:00:00-07:00', '2015-08-04T00:00:00-07:00', '2015-08-04T01:00:00-07:00', '2015-08-04T02:00:00-07:00', '2015-08-04T03:00:00-07:00', '2015-08-04T04:00:00-07:00', '2015-08-04T05:00:00-07:00', '2015-08-04T06:00:00-07:00', '2015-08-04T07:00:00-07:00', '2015-08-04T08:00:00-07:00', '2015-08-04T09:00:00-07:00', '2015-08-04T10:00:00-07:00', '2015-08-04T11:00:00-07:00', '2015-08-04T12:00:00-07:00', '2015-08-04T13:00:00-07:00', '2015-08-04T14:00:00-07:00', '2015-08-04T15:00:00-07:00', '2015-08-04T16:00:00-07:00', '2015-08-04T17:00:00-07:00', '2015-08-04T18:00:00-07:00', '2015-08-04T19:00:00-07:00', '2015-08-04T20:00:00-07:00', '2015-08-04T21:00:00-07:00', '2015-08-04T22:00:00-07:00', '2015-08-04T23:00:00-07:00', '2015-08-05T00:00:00-07:00', '2015-08-05T01:00:00-07:00', '2015-08-05T02:00:00-07:00', '2015-08-05T03:00:00-07:00', '2015-08-05T04:00:00-07:00', '2015-08-05T05:00:00-07:00', '2015-08-05T06:00:00-07:00', '2015-08-05T07:00:00-07:00', '2015-08-05T08:00:00-07:00', '2015-08-05T09:00:00-07:00', '2015-08-05T10:00:00-07:00', '2015-08-05T11:00:00-07:00', '2015-08-05T12:00:00-07:00', '2015-08-05T13:00:00-07:00', '2015-08-05T14:00:00-07:00', '2015-08-05T15:00:00-07:00', '2015-08-05T16:00:00-07:00', '2015-08-05T17:00:00-07:00', '2015-08-05T18:00:00-07:00', '2015-08-05T19:00:00-07:00', '2015-08-05T20:00:00-07:00', '2015-08-05T21:00:00-07:00', '2015-08-05T22:00:00-07:00', '2015-08-05T23:00:00-07:00', '2015-08-06T00:00:00-07:00', '2015-08-06T01:00:00-07:00', '2015-08-06T02:00:00-07:00', '2015-08-06T03:00:00-07:00', '2015-08-06T04:00:00-07:00', '2015-08-06T05:00:00-07:00', '2015-08-06T06:00:00-07:00', '2015-08-06T07:00:00-07:00', '2015-08-06T08:00:00-07:00', '2015-08-06T09:00:00-07:00', '2015-08-06T10:00:00-07:00', '2015-08-06T11:00:00-07:00', '2015-08-06T12:00:00-07:00', '2015-08-06T13:00:00-07:00', '2015-08-06T14:00:00-07:00', '2015-08-06T15:00:00-07:00', '2015-08-06T16:00:00-07:00', '2015-08-06T17:00:00-07:00', '2015-08-06T18:00:00-07:00', '2015-08-06T19:00:00-07:00', '2015-08-06T20:00:00-07:00', '2015-08-06T21:00:00-07:00', '2015-08-06T22:00:00-07:00', '2015-08-06T23:00:00-07:00', '2015-08-07T00:00:00-07:00', '2015-08-07T01:00:00-07:00', '2015-08-07T02:00:00-07:00', '2015-08-07T03:00:00-07:00', '2015-08-07T04:00:00-07:00', '2015-08-07T05:00:00-07:00', '2015-08-07T06:00:00-07:00', '2015-08-07T07:00:00-07:00', '2015-08-07T08:00:00-07:00', '2015-08-07T09:00:00-07:00', '2015-08-07T10:00:00-07:00', '2015-08-07T11:00:00-07:00', '2015-08-07T12:00:00-07:00', '2015-08-07T13:00:00-07:00', '2015-08-07T14:00:00-07:00', '2015-08-07T15:00:00-07:00', '2015-08-07T16:00:00-07:00']
number of end-valid-time entries = 168
probability-of-precipitation = ['0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '2', '2', '2', '2', '2', '2', '2', '2', '2', '2', '2', '2', '5', '5', '5', '5', '5', '5', '5', '5', '5', '5', '5', '5', '9', '9', '9', '9', '9', '9', '9', '9', '9', '9', '9', '9', '9', '9', '9', '9', '9', '9', '9', '9', '9', '9', '9', '9', '10', '10', '10', '10', '10', '10', '10', '10', '10', '10', '10', '10', '13', '13', '13', '13', '13', '13', '13', '13', '13', '13', '13', '13', '23', '23', '23', '23', '23', '23', '23', '23', '23', '23', '23', '23', '12', '12', '12', '12', '12', '12', '12', '12', '12', '12', '12', '12', '34', '34', '34', '34', '34', '34', '34', '34', '34', '34', '34']
number of probability-of-precipitation entries = 168
hourly-qpf = ['0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0.0050', '0.0050', '0.0050', '0.0050', '0.0050', '0.0050', '0.0033', '0.0033', '0.0033', '0.0033', '0.0033', '0.0033', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0.0017', '0.0017', '0.0017', '0.0017', '0.0017', '0.0017', '0.0083', '0.0083', '0.0083', '0.0083', '0.0083']
number of hourly-qpf entries = 168

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