Find the year which has highest number of active bonds - python

Given two lists:
Issue year of bonds
Maturity year of bond
Something like:
issue_year = [1934, 1932, 1945, 1946, ...]
mature_years = [1967, 1937, 1957, 1998, ...]
With this example, the first bond has issue-year of 1934, and maturity year of 1967, while the second bond has issue-year of 1932 and maturity year of 1937, and so on.
The problem I am trying to solve is to find the year which has the highest number of active bonds.
Here is what I have so far. This finds the year in which all bonds are active.
L1=[1936,1934,1937]
L2=[1940,1938,1940]
ctr=0
for i in range(len(L1)):
j=i
L3=list(range(L1[i],L2[j]))
if ctr==0:
tempnew=L3
else:
tempnew=list(set(L3) & set(tempnew))
ctr = ctr+1
Here tempnew is the intersection of all the active years for all the bonds. But, it might happen that the intersection of all the active years might be empty. For example, if bond 1 were active from 1932 through 1945, and bond 2 is active from 1947 thru 1960.
Can someone help ?


Here is some code which I believe meets your requirements. It works by scanning through issue and mature years list using zip. It then fills out a dict whose keys are all of the active years, and whose value are the number of bonds active that year. Finally it dumps all of the years which have the max number of active bonds:
Code:
def add_active_years(years, issue, mature):
for year in range(issue, mature+1):
years[year] = years.get(year, 0) + 1
# go through the lists and calculate the active years
years = {}
for issue, mature in zip(L1, L2):
add_active_years(years, issue, mature)
# now reverse the years dict into a count dict of lists of years
counts = {}
for year, count in years.items():
counts[count] = counts.get(count, []) + [year]
# show the result
print(counts[max(counts.keys())])
Sample Data:
L1 = [1936,1934,1937]
L2 = [1940,1938,1940]
Results:
[1937, 1938]

Related

Looping through multiple columns in a table in Python

I'm trying to loop through a table that contains covid-19 data. My table has 4 columns: month, day, location, and cases. The values of each column in the table is stored in its own list, so each list has the same length. (Ie. there is a month list, day list, location list, and cases list). There are 12 months, with up to 31 days in a month. Cases are recorded for many locations around the world. I would like to figure out what day of the year had the most total combined global cases. I'm not sure how to structure my loops appropriately. An oversimplified sample version of the table represented by the lists is shown below.
In this small example, the result would be month 1, day 3 with 709 cases (257 + 452).
Month
Day
Location
Cases
1
1
CAN
124
1
1
USA
563
1
2
CAN
242
1
2
USA
156
1
3
CAN
257
1
3
USA
452
.
.
...
...
12
31
...
...

I assume that you've put all the data in the same data frame, df.
df = pandas.DataFrame()
df['Month'] = name_of_your_month_list
df['Day'] = name_of_your_daylist
df['Location'] = name_of_your_location_list
df['Cases'] = name_of_your_cases_list
df.Cases.max() gives you the biggest number of cases. I assume that there is on year only in the dataset. So df[df.Cases==df.Cases.max()].index gives youth index that you search
For the the day, just filter :
df[df.index==df[df.Cases==df.Cases.max()].index].Day
For the month:
df[df.index==df[df.Cases==df.Cases.max()].index].Month
For the number of cases:
df[df.index==df[df.Cases==df.Cases.max()].index].Cases
For the country :
df[df.index==df[df.Cases==df.Cases.max()].index].Location
Reading the comment, it is not clear if you search the biggest cases in a Location or of the day. If its from the day, you'll have to filter first with a groupby('Day') function, to use it as groupby('Day').max()

You group your dataframe by month and day. Then iterate through the groups to find the group in which the sum of cases in all locations was max as shown below:
import pandas as pd
df = pd.DataFrame({'Month':[1,1,1,1,1,1], 'Day':[1,1,2,2,3,3],
'Location':['CAN', 'USA', 'CAN', 'USA','CAN', 'USA'],
'Cases':[124,563,242,156,257,452]})
grouped = df.groupby(['Month', 'Day'])
max_sum = 0
max_day = None
for idx, group in grouped:
if group['Cases'].sum() > max_sum:
max_sum = group['Cases'].sum()
max_day = group
month = max_day['Month'].iloc[1]
day = max_day['Day'].iloc[1]
print(f'Maximum cases of {max_sum} occurred on {month}/{day}.')
#prints: Maximum cases of 709 occurred on 1/3
If you don't want to use Pandas, this is how you do it:
months = [1,1,1,1,1,1]
days = [1,1,2,2,3,3]
locations = ['CAN', 'USA', 'CAN', 'USA','CAN', 'USA']
cases = [124,563,242,156,257,452]
dic = {}
target_day = 0
count = 0
for i in range(len(days)):
if days[i] != target_day:
target_day = days[i]
count = cases[i]
else:
count += cases[i]
dic[f'{months[i]}/{days[i]}'] = count
max_cases = max(dic.values())
worst_day = list(dic.keys())[list(dic.values()).index(max_cases)]
print(f'Maximum cases of {max_cases} occurred on {worst_day}.')
#Prints: Maximum cases of 709 occurred on 1/3.

you can check the max value in your cases list first. then map the max case's index with other three lists and obtain their values.
ex: caseList = [1,2,3,52,1,0]
the maximum is 52. its index is 3. in your case you can get the monthList[3], dayList[3],
locationList[3] respectively. then you get the relevant day, month and country which is having the most total global cases.
check whether this will help in your scenario.

You may use this strategy to get the required result.
daylist,monthlist,location,Cases = [1, 2, 3, 4], [1,1,1,1],['CAN','USA','CAN','USA'],[124,563,242,999]
maxCases = Cases.index(max(Cases))
print("Max Case:",Cases[maxCases])
print("Location:",location[maxCases])
print("Month:",monthlist[maxCases])
print("Day:",daylist[maxCases])

I have monthly data which I split up by all months for each year, how do I rebuild the data set to get back to normal, monthly data?

I have monthly data which I split up by all months by each year, so each month (january, feb, etc.) has 25 points (25 years worth of monthly data).
The reason why I split up the data set per month is because I wanted to detrend each month separately.
Now the issue I have now is how do I rebuild the data set to get back to normal, monthly data?
Just to clarify, the monthly data set is split up in January, Febuary, March etc. with 25 points of all the Januaries for each year (25 total years).
This is my function to split the months up:
def split_months(monthly_data):
month_split = []
for month in range(12):
month_split.append(monthly_data[month::12])
month_split = np.array(month_split)
return month_split
The function applied to my time series is:
lsds = split_months(ob_monthly(fwf_tot_grnl,26,28))
print(lsds.shape)
(12, 25)
The original time series is basically this exact array of values (each number representing monthly means):
data = np.array(
[ 4.44 3.614 4.0460005 5.1229997 8.181001 23.88
48.895 26.740002 7.454 9.884 5.093 4.2770004
3.8999999 4.9040003 3.736 4.4030004 22.261 40.200005
62.825005 47. 20.904001 10.974 5.723 4.5070004
4.04 3.5040002 3.7760003 8.803 19.691 25.249998
64.494995 44.36 8.723998 5.4339995 4.6829996 4.1770005
3.8999999 3.4340003 3.726 11.383001 19.711 39.320004
81.924995 44.109993 15.584 6.5639997 9.043 5.3770003
4.9 5.644 5.426 9.943001 22.291 30.459997
45.245003 30.32 15.934 5.694 5.873 4.597
4.6400003 3.654 3.9460003 8.813 21.901 46.29
54.90499 33.729996 14.974 7.3840003 5.9030004 5.0770006
4.4700007 3.764 4.586 10.782999 19.921 59.17
77.784996 41.09 13.904 5.244 4.183 4.077
4.596 5.039 4.853 10.633999 11.36 25.538
66.06901 60.221 9.421 5.9580007 7.3010006 5.1370006
9.871 5.959001 6.2910004 11.011 15.582 41.586002
61.489 62.522 22.592005 8.76 7.284001 6.6680017
6.3420005 5.6429996 6.397001 13.169999 15.686999 35.1
69.087 61.676994 16.506002 8.993002 9.748 9.421001
7.5270014 6.089 6.802001 7.0059996 25.265001 52.129
71.71501 37.315002 23.323002 8.122001 6.8900003 6.7650013
7.5070014 5.7620006 6.369001 9.704 16.115002 61.615997
78.908 75.03501 24.828001 18.709 8.946001 7.6440015
7.036001 8.021001 6.745001 8.9609995 25.263998 53.342
66.784 60.457996 12.041001 8.851002 7.6539993 7.332002
8.109002 9.958001 10.364 9.103999 25.901 49.302998
77.47001 47.663006 17.101002 9.033002 12.889999 8.370002
7.4340014 6.643001 8.225 8.264002 27.967001 30.463001
71.04099 66.920006 15.218003 10.14 7.986001 7.931002
11.101 6.612001 7.8220005 8.818002 15.4159975 57.508
95.218994 76.62101 15.224001 9.743 9.98 7.506002
7.056001 6.377001 8.582001 11.452001 26.950003 48.557995
80.06599 46.355 13.2630005 13.066001 10.836 8.853002
7.9060006 6.7550006 7.1250005 8.478 18.976002 39.144997
74.204994 46.373 10.978003 8.085002 7.340002 7.4710016
8.144002 6.551 7.689001 11.208 36.002 66.902
102.02799 82.63899 26.937 11.016 9.987 8.977001
8.089001 6.610002 7.6930013 7.0730014 16.036 50.027996
103.17498 66.486 13.222001 8.791002 8.938003 7.6330013
7.270001 7.9689994 9.035001 15.478998 25.383999 85.61101
131.645 70.778 19.850004 15.265 12.019999 9.633
8.530001 7.2450004 9.307002 11.766 11.969999 38.693996
69.27799 44.055004 14.650001 11.418 9.868003 8.571002
8.055 7.061002 7.7850013 7.9510007 21.322002 45.706993
87.51201 66.734 16.962 9.555001 8.364002 8.183001
7.443001 7.8109994 6.9640007 6.980001 14.530002 38.425
78.09 38.747 18.515001 10.052002 8.556001 7.909002
7.576001 6.868001 7.470001 17.761002 23.598999 55.842
100.045 65.67799 10.5130005 7.649001 7.543002 6.8360014])
Then you can apply my function split_months(data) to split the data into separate months like January (25 values), February (25 values) etc.
How do I go back to monthly data after splitting each month separately? Basically how would I return to the original data array (called data)?

I would apply a nested loop in which the data gets stored like this: First value first row, first value second row,... first value last row. Then second value first row etc... This returns your original data. I defined it as a function as well:
def merge_months(split_data):
merged_months = []
for value in range(len(split_data[0])):
for month in range(len(split_data)):
merged_months.append(split_data[month][value])
return merged_months
Is that what you're looking for? Note that this only works if all months have an equal amount of data points. Otherwise you need to change len(split_data[0])): in the first line of the nested loop.

Post Processing Aggregates

I have a question for a data analysis question where I need to separate data into two groups: months and states.
The task involves as:
In any given month, the minimum monthly rainfall for each state is the lowest rainfall recording from any weather station in that state during that month.
For example, suppose NSW had recordings of 1, 10, 5, and 7 for January; then its minimum monthly rainfall for January would be , the lowest of those recordings.
If there are several correct answers, your program can output any of the correct answers.
On the given data, the program's output should look like this:
Month: 11
State: QLD
What I have tried is:
minimum_monthly_rainfall = {}
is_first_line = True
for row in open("climate_data_2017.csv"):
if is_first_line:
is_first_line = False
else:
values = row.split(",")
month = values[0].split("-")[1]
rainfall = float(values[6])
state = values [1]
if month in minimum_monthly_rainfall:
minimum_monthly_rainfall[month].append(rainfall)
else:
minimum_monthly_rainfall[month] = [rainfall]
print(minimum_monthly_rainfall)
I am not sure as what to do now. I have skipped the first line of the data file which i will show below, and I have discriminated all the monthly rainfall into corresponding months. However I am not sure how I will separate this back into months? I know I shoul
The data file is:
Date,State,City,Station Code,Minimum temperature (C),Maximum temperature (C),Rainfall (mm),Evaporation (mm),Sunshine (hours),Direction of maximum wind gust,Speed of maximum wind gust (km/h),9am Temperature (C),9am relative humidity (%),3pm Temperature (C),3pm relative humidity (%)
2017-06-03,VIC,Melbourne,86338,9.9,14.3,0,,,SSW,19,10.8,76,13.1,73
2017-06-04,NSW,Sydney,66062,10.5,19.7,1.4,0.6,6.8,W,28,12.2,77,19,57
2017-06-04,QLD,Brisbane,40913,12.8,22.6,0.6,1.8,9.1,ENE,31,16.3,77,22.5,52
2017-06-04,VIC,Melbourne,86338,4.4,15.8,0,,,NE,13,5.7,100,15.5,53
2017-06-05,NSW,Sydney,66062,9.4,19.6,0.5,2.8,9.4,W,28,11.5,78,19.2,41
2017-06-05,QLD,Brisbane,40913,10.1,25.3,0.6,1.8,9.2,ENE,31,15,71,24.9,30
2017-06-05,VIC,Melbourne,86338,5.6,17.5,0,,,NW,28,8.7,82,16.8,57
2017-06-06,NSW,Sydney,66062,9.1,17.9,0.5,2.2,7.2,WSW,59,11.2,58,14.7,43
2017-06-06,QLD,Brisbane,40913,9.2,25.2,0.6,2.4,7.7,N,41,17.9,38,24.5,13
2017-06-06,VIC,Melbourne,86338,8.7,13.9,6.4,,,SSW,43,11.8,78,12.6,68
2017-06-07,NSW,Sydney,66062,8.8,15.5,54.6,2.6,0,SSW,56,13.3,82,14.2,85
2017-06-07,QLD,Brisbane,40913,5,22.8,0.6,4.4,9.1,SE,31,12.4,68,22.5,39
2017-06-07,VIC,Melbourne,86338,5.9,14.3,0,,,SE,28,8.3,91,14,54
2017-06-08,NSW,Sydney,66062,12.4,18.1,61,1.8,1.3,SE,44,13.3,91,16.9,84
2017-06-08,QLD,Brisbane,40913,9.9,22.5,0.6,2.2,9.2,ENE,48,13,75,22.1,25
2017-06-08,VIC,Melbourne,86338,3.6,15,0,,,SSW,30,6.6,86,14,56
2017-06-09,NSW,Sydney,66062,10.4,18.9,3.4,1.6,4.8,ESE,52,12.5,90,18.2,64
2017-06-09,QLD,Brisbane,40913,7.7,24.1,0.6,4.8,9.3,NE,37,15.8,27,23.8,14
2017-06-09,VIC,Melbourne,86338,6.6,15,1.8,,,S,22,11,98,13.9,66
2017-06-10,NSW,Sydney,66062,12.5,18.1,38.8,3.6,0.2,SSE,50,15.8,85,17.5,70
2017-06-10,QLD,Brisbane,40913,7.7,20.8,0.6,3.8,1.9,NNE,33,14.7,33,20.4,25
2017-06-10,VIC,Melbourne,86338,7,15.8,0,,,SSW,17,7.5,100,14,68
2017-06-11,NSW,Sydney,66062,12.5,17.2,2.8,1.6,0.2,WNW,20,13.1,90,16,85
2017-06-11,QLD,Brisbane,40913,11.4,23.5,0.6,3.2,4.5,ENE,41,13.6,33,23.4,14
2017-06-11,VIC,Melbourne,86338,3.4,15.5,0.2,,,SSW,22,6.1,96,13.6,67
2017-06-12,NSW,Sydney,66062,11.3,20.4,1.2,0.8,7.5,SSE,26,11.6,92,19.1,59
2017-06-12,QLD,Brisbane,40913,10.1,24,0.6,3.8,7.6,NNE,39,16.7,26,23.7,22
2017-06-12,VIC,Melbourne,86338,6,15.9,0,,,SSW,19,10,85,15,77
2017-06-13,NSW,Sydney,66062,9.5,18.7,0.5,2.2,9.1,SSE,37,11.2,82,18.2,62
2017-06-13,QLD,Brisbane,40913,10.8,24.8,0.6,3.4,5.6,NE,28,15,35,22.4,24
2017-06-13,VIC,Melbourne,86338,8.8,15.1,0,,,WSW,20,10.7,88,13.4,67
2017-06-14,NSW,Sydney,66062,11.1,17.8,2.6,1.8,2.9,WNW,24,13.3,82,17.4,78
2017-06-14,QLD,Brisbane,40913,12.1,20.4,14.6,3.4,4.3,E,37,14.2,83,19.4,63
2017-06-14,VIC,Melbourne,86338,4,17.4,0,,,N,28,7.8,97,16.7,56
2017-06-15,NSW,Sydney,66062,10.7,20.1,0.6,1.2,6.4,W,22,11.9,89,18.7,69
2017-06-15,QLD,Brisbane,40913,9.9,19.3,0.6,3.2,6.5,SSW,24,14.7,87,18.1,65
2017-06-15,VIC,Melbourne,86338,7.7,19,0,,,N,20,9.8,87,17.4,53
2017-06-16,NSW,Sydney,66062,11.9,17.3,0.5,1.6,0.2,WNW,26,13.3,79,17.2,69
2017-06-16,QLD,Brisbane,40913,6.6,20.8,0.6,1,8.3,S,22,12.1,85,20.2,43
2017-06-16,VIC,Melbourne,86338,6.6,17.1,0,,,NNE,15,8.4,86,15.9,65
2017-06-17,NSW,Sydney,66062,13.2,19.1,0.5,1,0.2,SSW,26,14.6,81,17.4,67
2017-06-17,QLD,Brisbane,40913,5,21.7,0.6,2.4,9,E,39,13.1,78,21.5,47
2017-06-17,VIC,Melbourne,86338,4.2,15.5,0,,,NE,15,5.4,95,14.9,66
2017-06-18,NSW,Sydney,66062,11.3,18,1.8,2,6.3,S,52,12.9,83,17.6,62
2017-07-02,NSW,Sydney,66062,5.4,17.6,0.5,2,9.5,W,28,7.6,78,16.1,44
2017-07-02,QLD,Brisbane,40913,3.9,15,20,1.6,4.2,S,35,7.3,90,14.2,46
2017-07-02,VIC,Melbourne,86338,0.8,14.3,0,,,N,41,3.8,75,13.9,36
2017-07-03,NSW,Sydney,66062,5.6,15.8,0.5,2.6,2.7,N,31,7.9,81,15.4,57
2017-07-03,QLD,Brisbane,40913,7,16.6,0.6,2,4.6,NNW,31,8.9,82,15.2,58
2017-07-03,VIC,Melbourne,86338,3.8,13.9,0,,,NNE,39,9,60,11.2,92
2017-07-04,NSW,Sydney,66062,7.9,21.8,0.2,0.6,9.3,,,13.2,76,21.5,23
2017-07-04,QLD,Brisbane,40913,5.5,14.4,2,1.4,1.2,N,19,8.3,94,13.9,73
2017-07-04,VIC,Melbourne,86338,9,16.2,3,,,N,33,11,78,15.6,54
2017-07-05,NSW,Sydney,66062,12.2,20.8,0.5,5.4,9.2,W,52,14.4,51,18.7,36
2017-07-05,QLD,Brisbane,40913,8,13.9,3.4,1.8,2.8,SW,59,11.9,92,10.7,83
2017-07-05,VIC,Melbourne,86338,10.1,15.8,0,,,SSW,24,12.4,75,13.4,65
2017-07-06,NSW,Sydney,66062,9.1,18.2,0.5,4.8,9.7,WSW,37,11.5,62,17.4,36
2017-07-06,QLD,Brisbane,40913,4.7,16,10.4,4.2,5.5,WNW,30,7.4,88,14.4,60
2017-07-06,VIC,Melbourne,86338,4.8,15,0,,,N,28,8.1,75,14.1,46
2017-07-07,NSW,Sydney,66062,6.6,19.1,0.5,3.6,9.4,WNW,35,8.8,73,18.6,24
2017-07-07,QLD,Brisbane,40913,7.4,17.6,9.8,1,6.9,SW,33,11.6,97,16.2,59
2017-07-07,VIC,Melbourne,86338,8,14.2,0,,,NNW,37,9,79,13.4,56
2017-07-08,NSW,Sydney,66062,7,18.1,0.5,3.2,9.3,W,37,9,66,17,36
2017-07-08,QLD,Brisbane,40913,3.2,17.2,0.6,1.4,9,S,28,10.2,83,16.5,41
2017-07-08,VIC,Melbourne,86338,5.3,13.5,0,,,NW,30,9,84,13.2,58
2017-07-09,NSW,Sydney,66062,7.4,18,0.5,2,9.5,W,33,9.2,69,17.6,33
2017-07-09,QLD,Brisbane,40913,7.1,17.8,0.6,2.4,7.4,NE,22,10.4,68,17.4,42
2017-07-09,VIC,Melbourne,86338,7.3,14.6,0.8,,,NNW,39,9.7,70,13.3,49
2017-07-10,NSW,Sydney,66062,7,17.3,0.5,3.6,9.4,W,39,9.4,67,16.7,36
2017-07-10,QLD,Brisbane,40913,5.9,17.9,0.6,2.4,4,NNE,39,12.5,52,17.6,40
2017-07-10,VIC,Melbourne,86338,7,16,0,,,W,28,9.2,74,15.2,48
2017-07-11,NSW,Sydney,66062,6.6,18.4,0.5,2.8,9.2,SSW,39,9,63,17.7,46
2017-07-11,QLD,Brisbane,40913,12.5,19.7,0.6,2.6,4.2,WNW,52,15.6,52,18.5,78
2017-07-11,VIC,Melbourne,86338,6.9,14.3,0.4,,,NW,19,9.8,79,13.8,57
2017-07-12,NSW,Sydney,66062,7.8,14.8,1.6,3.4,0.5,W,31,8.9,76,13,79
2017-07-12,QLD,Brisbane,40913,14.8,20.4,3.4,2.4,8,NW,61,18,69,19.2,59
2017-07-12,VIC,Melbourne,86338,3.2,15.1,0,,,N,30,4.8,89,14.7,49
2017-07-13,NSW,Sydney,66062,8,17.4,10.4,1,9.3,ENE,30,9.6,80,17.2,49
2017-07-13,QLD,Brisbane,40913,13.2,18.6,7.2,3.6,3,SW,33,15.1,96,18.1,60
2017-07-13,VIC,Melbourne,86338,4.8,13.3,0,,,N,56,10.9,56,13,54
2017-07-14,NSW,Sydney,66062,9.6,20.8,0.5,4,6.1,NNW,30,12.2,67,19.3,41
2017-07-14,QLD,Brisbane,40913,12.3,19.2,0.4,2.8,2.3,NE,24,14.6,76,18.1,62
2017-07-14,VIC,Melbourne,86338,9.9,15.9,2,,,N,50,10.5,76,15.2,41
2017-07-15,NSW,Sydney,66062,12.1,19.8,0.4,4,6.1,SW,30,13,71,17.9,39
2017-07-15,QLD,Brisbane,40913,11.4,19.9,0.6,2.6,1.3,WNW,50,14,96,16.9,82
2017-07-15,VIC,Melbourne,86338,4.5,12,0,,,NW,22,7.8,71,11.6,58
2017-07-16,NSW,Sydney,66062,7.7,18.4,0.5,1.8,9.4,WSW,31,9.4,61,16.8,32
2017-07-16,QLD,Brisbane,40913,13.7,19.2,4.6,2,3.6,SW,37,15.8,66,18.1,45
2017-07-16,VIC,Melbourne,86338,3,12.5,0,,,N,52,5.7,75,11.1,54
2017-07-17,NSW,Sydney,66062,7.9,19.3,0.5,4.2,6.5,W,24,9.8,75,17.3,44
2017-07-17,QLD,Brisbane,40913,8.7,19.2,0.6,3.4,9.1,NE,33,12.4,64,18.5,41
2017-07-17,VIC,Melbourne,86338,5.7,15.3,0,,,N,52,11.8,55,14.6,50
2017-07-18,NSW,Sydney,66062,9.1,22.9,0.5,1.2,9.8,NW,54,14.1,62,22.6,30
2017-07-18,QLD,Brisbane,40913,11.2,24.6,0.6,3.2,9.2,N,46,14.8,52,24.1,35
2017-07-18,VIC,Melbourne,86338,9.7,14.5,0.2,,,NNW,41,10.6,75,13.8,55
2017-07-19,NSW,Sydney,66062,10.6,17.5,0.5,6.6,9.1,WSW,57,12,53,16.9,33
2017-07-19,QLD,Brisbane,40913,13.2,18.7,8.4,3.8,3,N,61,14,96,14.1,89
2017-07-19,VIC,Melbourne,86338,8.9,13.2,5,,,SSW,37,10.8,99,11.6,81
2017-07-20,NSW,Sydney,66062,9.3,17.5,0.5,4.4,9.6,WSW,63,11.4,55,15.8,26
2017-07-20,QLD,Brisbane,40913,11.5,18.2,18.4,3.6,7.7,WSW,56,15.3,69,16.4,71
2017-07-20,VIC,Melbourne,86338,7.9,10.7,0.6,,,SSW,39,9.4,82,9.7,77
2017-07-21,NSW,Sydney,66062,7.5,18,0.5,4.2,9.2,W,46,9.6,53,17.4,37
2017-07-21,QLD,Brisbane,40913,10.6,20.1,5,0.6,7,NW,59,13.2,88,18.6,53
2017-07-21,VIC,Melbourne,86338,3.3,13.8,4.2,,,N,26,5.8,93,13.4,53
2017-07-22,NSW,Sydney,66062,6,19.1,0.5,3.6,9.8,W,31,7.9,64,18.7,27
2017-07-22,QLD,Brisbane,40913,10.7,19.3,6.1,,4.5,WSW,31,12.4,97,17.8,55
2017-07-22,VIC,Melbourne,86338,5.4,12.4,0,,,N,59,8.4,65,11.6,54
2017-07-23,NSW,Sydney,66062,6.5,22.1,0.5,3.6,9.9,NW,44,10.8,49,22.1,22
2017-07-23,QLD,Brisbane,40913,11.7,18.1,2.3,6.6,4.8,N,50,13.4,93,18,66
2017-07-23,VIC,Melbourne,86338,8.4,14.1,0,,,N,44,10.2,57,11.3,90
2017-07-24,NSW,Sydney,66062,10,21.3,0.5,7.2,9.6,WSW,31,12.7,51,20.9,24
2017-07-24,QLD,Brisbane,40913,10,18.6,16.8,4.2,3.9,W,39,12.4,95,17.1,50
2017-07-24,VIC,Melbourne,86338,8.1,16.1,7,,,WSW,28,9.9,77,15.2,53
2017-07-25,NSW,Sydney,66062,7.9,20.6,0.5,4.2,9.9,NW,30,11,63,19.7,32
2017-07-25,QLD,Brisbane,40913,10.4,19.6,3,0,3.4,NW,39,13,96,18.7,53
2017-07-25,VIC,Melbourne,86338,9.8,17.2,0,,,N,48,11.1,72,16.4,53
2017-07-26,NSW,Sydney,66062,11,21.3,0.5,2.8,9.9,W,70,18,39,20.2,27
2017-07-26,QLD,Brisbane,40913,11.7,19.2,0.4,2.2,3.1,SW,48,15.6,78,17.4,73
2017-07-26,VIC,Melbourne,86338,10.9,15,2.4,,,SW,37,12.5,70,12.7,71
2017-07-27,NSW,Sydney,66062,9.1,17.5,0.5,4.8,9.7,W,31,11.2,61,17.1,44
2017-07-27,QLD,Brisbane,40913,11.6,21.5,24,1.8,6.5,NNW,59,14.5,90,19.4,68
2017-07-27,VIC,Melbourne,86338,4.4,14.2,2,,,N,52,6.2,90,13.8,55
2017-07-28,NSW,Sydney,66062,11.2,19.1,0.5,4.2,8.3,WSW,67,13.1,51,18.9,25
2017-07-28,QLD,Brisbane,40913,14.5,17.7,10.8,3.4,,WNW,78,17,60,13.9,83
2017-07-28,VIC,Melbourne,86338,6.2,14.3,2.8,,,NNE,37,10.9,73,13.4,56
2017-07-29,NSW,Sydney,66062,8.2,21.2,0.5,4.6,9.9,W,31,10.7,52,20.9,23
2017-07-29,QLD,Brisbane,40913,9.7,15.1,11.2,6.8,5,WSW,72,12.1,77,12.3,86
2017-07-29,VIC,Melbourne,86338,9.7,17.9,0,,,N,81,12.7,51,16.9,41
2017-07-30,NSW,Sydney,66062,10.7,26.5,0.5,6,6.2,NNW,44,18.4,25,24.8,22
2017-07-30,QLD,Brisbane,40913,5.3,17.1,9.8,1.6,6.5,WSW,33,11.1,79,15.4,52
2017-07-30,VIC,Melbourne,86338,11.3,18.1,0,,,NW,31,13.7,64,17.5,34
2017-07-31,NSW,Sydney,66062,15.6,16.5,0.5,4.4,0,WSW,56,15.7,72,11.8,85
2017-07-31,QLD,Brisbane,40913,9.5,15.7,0.6,2.2,0.6,WSW,67,12.3,75,12.8,81
2017-07-31,VIC,Melbourne,86338,6.7,14.8,0,,,S,19,9,79,13.7,56
2017-08-01,NSW,Sydney,66062,8.6,18.1,6,2.4,9.8,SW,39,11.6,57,16.9,39
2017-08-01,QLD,Brisbane,40913,1.8,15.3,23.8,2.6,9,SSW,39,8.4,87,14.2,53
2017-08-01,VIC,Melbourne,86338,3.5,15.5,0,,,SSW,20,6.2,89,13,64
2017-08-02,NSW,Sydney,66062,7.2,18.2,0.5,3.2,8.8,S,41,9.6,62,16.8,51
2017-08-02,QLD,Brisbane,40913,1,14.9,0.6,0.6,9.7,E,26,8.7,81,14.3,47
2017-08-02,VIC,Melbourne,86338,3.4,12.9,0,,,NNE,13,5.6,100,12.8,61
2017-08-03,NSW,Sydney,66062,8.9,17.4,3,2.8,5.5,NNE,37,10.3,85,16.4,70
2017-08-03,QLD,Brisbane,40913,3.6,17.9,0.2,1.8,2.1,W,54,8.5,87,14.3,96
2017-08-03,VIC,Melbourne,86338,3.1,9,0,,,NNE,15,4.3,89,8.7,84
2017-08-04,NSW,Sydney,66062,10.3,17.3,13.8,1.8,10.2,W,50,12.8,60,16.6,41
2017-08-04,QLD,Brisbane,40913,7.9,15.5,1.6,2,1,WSW,30,10.3,77,14.3,64
2017-08-04,VIC,Melbourne,86338,4.3,12.1,6.6,,,N,50,8.9,87,11.5,66
2017-08-05,NSW,Sydney,66062,9.6,19.9,0.5,5.6,10.1,WNW,48,14,46,19.3,22
2017-08-05,QLD,Brisbane,40913,9.3,18.3,0.4,1.6,6.1,WSW,31,12.4,94,16.8,59
2017-08-05,VIC,Melbourne,86338,8.8,14.2,0,,,N,52,10.6,68,13.1,55
2017-08-06,NSW,Sydney,66062,12.1,21.3,0.5,6.8,9.9,NW,54,15,46,20.9,26
2017-08-06,QLD,Brisbane,40913,6.7,19.8,0.6,3.2,6.1,NW,37,11.8,81,19.4,41
2017-08-06,VIC,Melbourne,86338,9,13,0,,,NNE,54,10,76,11.7,54
2017-08-07,NSW,Sydney,66062,13.5,18.4,0.5,6.8,9.8,W,63,14.7,44,16.9,25
2017-08-07,QLD,Brisbane,40913,10.6,19.4,0.6,2.4,0.8,N,30,14.5,78,18.4,65
2017-08-07,VIC,Melbourne,86338,7.7,12.3,4.2,,,SW,41,9.4,70,11,79
2017-08-08,NSW,Sydney,66062,9.7,19.3,0.5,7.2,10.3,WSW,54,12.1,45,18.5,24
2017-08-08,QLD,Brisbane,40913,12.5,19.2,0.4,2,2.3,NW,57,15.9,82,14.8,93
2017-08-08,VIC,Melbourne,86338,7.9,15,2.2,,,NW,26,10.5,72,13.9,59
2017-08-09,NSW,Sydney,66062,9.2,20.9,0.5,5,10.4,W,33,12,53,20,32
2017-08-09,QLD,Brisbane,40913,8.5,14.7,43.6,4.4,3,SW,72,9.7,93,11.6,63
2017-08-09,VIC,Melbourne,86338,8.9,16.8,0,,,N,46,11.4,74,15.1,60
2017-08-10,NSW,Sydney,66062,9.2,24.2,0.5,4,9.4,NNW,31,11.8,64,23.5,24017-09-10,NSW,Sydney,66062,8,19.1,0.5,5,10.8,W,24,13.5,52,17.3,51
2017-09-10,QLD,Brisbane,40913,10.6,20.8,0.2,4.6,4.7,W,30,14.6,91,20.3,46
2017-09-10,VIC,Melbourne,86338,5.3,16.1,0.2,,,N,20,10.8,76,16,47
2017-09-11,NSW,Sydney,66062,8.7,22.7,0.5,3.8,10.1,NE,24,14.4,60,22.5,29
2017-09-11,QLD,Brisbane,40913,11.2,19.6,5.2,4.4,5.6,WSW,41,16.4,94,17.8,73
2017-09-11,VIC,Melbourne,86338,8.1,18.1,0,,,N,46,12.7,63,16.9,50
2017-09-12,NSW,Sydney,66062,11.3,27.2,0.5,5.8,2.3,E,26,18.2,43,23.4,34
2017-09-12,QLD,Brisbane,40913,13.2,19.4,1.4,4.2,7.9,SW,54,15.9,68,19,35
2017-09-12,VIC,Melbourne,86338,12.2,19.9,0.2,,,NNW,39,15.2,60,19.7,59
2017-09-13,NSW,Sydney,66062,18.2,33.8,0.5,6.2,9.6,NNW,70,24.9,19,32.5,10
2017-09-13,QLD,Brisbane,40913,7.7,21.6,0.6,6.4,11,E,43,13.3,50,21.6,27
2017-09-13,VIC,Melbourne,86338,13.3,16.4,2,,,WSW,50,14.9,62,11.7,69
2017-09-14,NSW,Sydney,66062,12,17.3,0.2,13,10.5,WSW,72,13.8,37,16.4,26
2017-09-14,QLD,Brisbane,40913,6.3,25.6,0.6,6,7,WSW,30,17.2,34,24.1,27
2017-09-14,VIC,Melbourne,86338,5.8,15.3,1.4,,,W,43,9.6,66,14.1,56
2017-09-15,NSW,Sydney,66062,10.5,22.8,0.5,7.8,10.6,W,52,16.6,37,22.2,24
2017-09-15,QLD,Brisbane,40913,14.5,27.8,0.6,4.6,8.5,ENE,37,17.6,43,27.6,26
2017-09-15,VIC,Melbourne,86338,9.6,16.7,0,,,WSW,35,13.5,58,14.8,82
2017-09-16,NSW,Sydney,66062,12.5,24.2,0.5,7,6.4,SSW,56,17.3,43,21.3,22
2017-09-16,QLD,Brisbane,40913,10.9,24.4,0.6,4.8,6.5,WSW,33,15,96,21.9,48
2017-09-16,VIC,Melbourne,86338,7.4,13.3,21.4,,,SSW,54,9.1,81,12.5,52
2017-09-17,NSW,Sydney,66062,8.1,18.6,0.5,4.6,10.8,ESE,28,13.6,48,16.4,37
2017-09-17,QLD,Brisbane,40913,14.6,21.7,0.4,4.4,9.6,WSW,33,18.5,72,21.1,47
2017-09-17,VIC,Melbourne,86338,4,18.1,0.2,,,N,43,10.5,58,17.8,35
2017-09-18,NSW,Sydney,66062,8.2,21,0.5,5.6,10.7,NE,39,14,55,20.2,55
2017-09-18,QLD,Brisbane,40913,7.6,26,0.6,4,10.4,E,37,15.8,71,25.7,34
2017-09-18,VIC,Melbourne,86338,10.5,22,0,,,NNW,63,15.7,32,20.9,30
2017-09-19,NSW,Sydney,66062,14,25.2,0.5,6.4,10.8,SSW,54,20.4,30,23.1,16
2017-09-19,QLD,Brisbane,40913,8.9,22.4,0.6,7,8.5,WSW,31,17.9,73,21,67
2017-09-19,VIC,Melbourne,86338,9,13.9,0.2,,,WNW,31,11.6,58,12.4,58
2017-09-20,NSW,Sydney,66062,10.7,19,0.5,9.2,7.7,WSW,33,14.6,44,17.7,56
2017-09-20,QLD,Brisbane,40913,14.7,21.4,5.4,4.6,6.9,W,48,18,57,20.6,52
2017-09-20,VIC,Melbourne,86338,6,19.2,0,,,N,35,11.6,63,18.6,42
2017-09-21,NSW,Sydney,66062,10.4,21.3,0.5,3.8,10.1,NE,30,15.6,65,21.2,49
2017-09-21,QLD,Brisbane,40913,16.8,21.5,5.8,5,3.4,NW,81,19.8,72,19,61
2017-09-21,VIC,Melbourne,86338,11.6,23.6,0,,,N,54,14.8,46,22.4,33
2017-09-22,NSW,Sydney,66062,12.8,27.7,0.5,7.2,10.5,NNE,35,20.1,35,25.8,26
2017-09-22,QLD,Brisbane,40913,13,19.2,7.4,5.2,1.9,W,89,14.5,88,17.3,58
2017-09-22,VIC,Melbourne,86338,12.8,25.6,0,,,N,33,14.9,71,24.8,32
2017-09-23,NSW,Sydney,66062,15.5,32.2,0.5,8.6,5.7,NNE,50,23,25,29.1,25
2017-09-23,QLD,Brisbane,40913,11.4,17.1,9.8,5.4,5.3,SW,52,13.4,72,16.1,48
2017-09-23,VIC,Melbourne,86338,14.8,30.6,0,,,N,61,21.1,30,30,24
2017-09-24,NSW,Sydney,66062,23,29.2,0.5,12,6.5,W,54,27.1,20,27.8,15
2017-09-24,QLD,Brisbane,40913,10.1,17.3,5.2,4.4,4.5,NNW,46,14.8,75,14,87
2017-09-24,VIC,Melbourne,86338,12.9,19.9,0.4,,,NNW,56,14.9,56,18.4,40
2017-09-25,NSW,Sydney,66062,16,26.7,0.5,10.8,10.8,WNW,57,22.3,21,23.3,19
2017-09-25,QLD,Brisbane,40913,9.3,17,10.4,3.2,4.6,NE,35,11.8,81,14.9,67
2017-09-25,VIC,Melbourne,86338,10.3,14.2,1.6,,,SSW,35,12.9,64,12.8,68
2017-09-26,NSW,Sydney,66062,12.3,22.2,0.5,9.6,11,WSW,41,19.5,29,19,47
2017-09-26,QLD,Brisbane,40913,6,18.2,3.4,4.4,9.3,WSW,41,13.1,69,17.5,40
2017-09-26,VIC,Melbourne,86338,5,15.1,0.4,,,S,24,11.3,64,14,46
2017-09-27,NSW,Sydney,66062,15.4,22.7,0.5,6.6,8.2,ENE,50,18.1,58,21.1,62
2017-09-27,QLD,Brisbane,40913,5,16.7,0.6,6.8,5.6,SW,41,13.1,58,14.7,56
2017-09-27,VIC,Melbourne,86338,9.6,24.6,0,,,NNW,50,12.5,64,24.5,30
2017-09-28,NSW,Sydney,66062,18,25.7,0.5,6.2,3.8,WNW,59,22.7,47,24.1,26
2017-09-28,QLD,Brisbane,40913,6.9,16.6,1,3.4,3.5,WSW,33,12.7,72,13.5,76
2017-09-28,VIC,Melbourne,86338,10.4,18.5,0,,,SW,35,14.1,66,16.8,45 2017-10-01,VIC,Melbourne,86338,9.7,17.2,0,,,S,24,12.4,65,16.4,58
2017-10-02,NSW,Sydney,66062,12,22.9,0.5,6.2,10.1,NE,44,16.7,55,21.3,60
2017-10-02,QLD,Brisbane,40913,7.7,24,0.6,4.8,11.3,WSW,39,19.6,43,22.1,45
2017-10-02,VIC,Melbourne,86338,9.5,16.4,0,,,SSW,24,12.1,75,15.2,58
2017-10-15,NSW,Sydney,66062,16,22.4,0.5,3.8,2.4,ESE,28,22.3,52,20.1,58
2017-10-15,QLD,Brisbane,40913,16.4,25.4,0.6,7.2,11.6,W,33,20.4,61,22.4,53
2017-10-15,VIC,Melbourne,86338,6.8,17.6,0,,,SSW,31,12.4,67,16.7,67
2017-10-16,NSW,Sydney,66062,15.9,23.3,0.5,5,10.4,E,30,22.4,55,22.3,50
2017-10-16,QLD,Brisbane,40913,15.3,19.9,3,5.8,6.5,NW,91,15.8,91,18.2,51
2017-10-16,VIC,Melbourne,86338,11.3,28.6,0,,,NNE,28,12.9,91,26.2,40
2017-10-17,NSW,Sydney,66062,16.9,23.5,0.5,7.6,9.9,ENE,39,21.2,57,22.9,47
2017-10-17,QLD,Brisbane,40913,13.9,20.1,7.6,5.4,5.8,WNW,52,17,71,19.8,50
2017-10-17,VIC,Melbourne,86338,12.8,30.2,0,,,N,37,21,50,30.1,24
2017-10-18,NSW,Sydney,66062,18.8,23.2,0.5,9,10.4,NNE,50,20.9,64,22.5,56
2017-10-18,QLD,Brisbane,40913,12,19.4,5,5,11.7,SW,44,16.7,46,18.2,37
2017-10-18,VIC,Melbourne,86338,14.5,30.9,0,,,NNW,48,21.7,43,29.9,31
2017-10-19,NSW,Sydney,66062,17.1,25.3,0.5,10,11.4,NNE,52,22.1,52,24.6,47
2017-10-19,QLD,Brisbane,40913,6.8,24.7,0.6,6.4,12.1,E,56,16.8,44,24.4,24
2017-10-19,VIC,Melbourne,86338,21.7,25.6,0,,,SW,43,23.8,45,23.5,71
2017-10-20,NSW,Sydney,66062,19,19.6,7.8,9.2,0,S,56,19.6,87,15.3,85
2017-10-20,QLD,Brisbane,40913,12.1,29.9,0.6,6,12.1,E,61,22.3,31,29.4,17
2017-10-20,VIC,Melbourne,86338,11.3,16.2,2.6,,,S,41,13.4,69,15.3,56
2017-10-21,NSW,Sydney,66062,13,20,16.4,4.4,11.3,SSW,50,16.8,52,18.9,53
2017-10-21,QLD,Brisbane,40913,15.9,29.5,0.6,9.2,4.9,ENE,65,23.2,32,28.2,26
2017-10-21,VIC,Melbourne,86338,11,15.4,0,,,S,28,11.8,71,14.8,63
2017-10-22,NSW,Sydney,66062,13.4,21.7,0.2,4.8,5.5,ESE,30,16,75,18.7,60
2017-10-22,QLD,Brisbane,40913,18.5,28.4,1.2,7.2,11.7,E,70,23.1,56,27.1,46
2017-10-22,VIC,Melbourne,86338,11.1,16,0,,,SSW,28,12.7,78,15.2,76
2017-10-23,NSW,Sydney,66062,12.7,23,3.4,3.6,12.1,S,31,18,60,22.1,43
2017-10-23,QLD,Brisbane,40913,14.3,23.6,0.6,5.6,3.8,WSW,37,18.2,80,22.4,57
2017-10-23,VIC,Melbourne,86338,11.9,19,0,,,SSW,31,13.6,72,17.3,61
2017-10-24,NSW,Sydney,66062,13.9,24.3,0.5,6.8,10.5,NE,39,19.9,57,22.6,55
2017-10-24,QLD,Brisbane,40913,14.3,22.6,0.6,6.2,11,W,37,18.2,63,20.3,40
2017-10-24,VIC,Melbourne,86338,12.1,20.5,0.2,,,SSW,26,15,74,17.4,61
2017-10-25,NSW,Sydney,66062,18,26.9,0.5,8,5,S,59,21,67,23.3,56
2017-10-25,QLD,Brisbane,40913,9.6,22.7,0.6,7,7.3,W,44,19,69,21.7,54
2017-10-25,VIC,Melbourne,86338,13.3,18.5,11.4,,,SSW,24,14.5,94,17.3,67
2017-10-26,NSW,Sydney,66062,17.2,24.6,0.4,7,6.5,WSW,63,19.7,73,22.6,63
2017-10-26,QLD,Brisbane,40913,13.3,20.9,1.2,4.6,5.3,WNW,67,17.6,77,16.4,66
2017-10-26,VIC,Melbourne,86338,12.9,16.8,0.2,,,SSW,39,13.8,83,15.5,73
2017-10-27,NSW,Sydney,66062,14.8,24.2,34.2,7.2,3.9,WSW,61,18.1,85,22.3,65
2017-10-27,QLD,Brisbane,40913,8.4,19.3,2,6.2,4.8,WSW,44,16.7,48,17.5,51
2017-10-27,VIC,Melbourne,86338,10.3,27.4,0,,,N,48,15.1,78,25.2,37
2017-10-28,NSW,Sydney,66062,17.5,25.1,0.2,3.2,7.7,ENE,35,21.6,66,22.3,65
2017-10-28,QLD,Brisbane,40913,14,20.5,1,4.2,6.2,WSW,52,18.4,54,19.8,54
2017-10-28,VIC,Melbourne,86338,15,21.4,0.4,,,N,39,15.4,72,19.7,46
2017-10-29,NSW,Sydney,66062,20.2,29.6,0.2,4.6,10.6,NNE,39,24.4,49,27.7,35
2017-10-29,QLD,Brisbane,40913,12.6,20.6,0.8,4.2,11.6,SW,54,16.7,46,19.9,41
2017-10-29,VIC,Melbourne,86338,11.5,28.6,0,,,N,61,19,63,27.4,25
2017-10-30,NSW,Sydney,66062,20.3,35.4,0.5,11.4,9.3,SSW,69,28.5,37,34.9,13
2017-10-30,QLD,Brisbane,40913,7.9,27.5,0.6,6.8,12.5,WSW,35,19.1,44,25.9,33
2017-10-30,VIC,Melbourne,86338,8.8,17,4.4,,,W,46,12.6,62,14.4,55
2017-10-31,NSW,Sydney,66062,13,20.5,0.5,12,10.1,SSE,57,15.8,43,19.7,36
2017-10-31,QLD,Brisbane,40913,8.7,29.3,0.6,10.4,12.2,SW,39,20.2,54,27.1,35
2017-10-31,VIC,Melbourne,86338,8.1,16.4,6,,,S,28,12,62,15.4,48
2017-11-01,NSW,Sydney,66062,13,22.2,0.5,9.6,4.2,SSW,54,15.4,39,21,32
2017-11-01,QLD,Brisbane,40913,15,30.5,0.6,10.4,10.4,SE,44,21.5,48,29,39
2017-11-01,VIC,Melbourne,86338,11.9,18,0.4,,,SSW,26,14.7,78,15.8,66
2017-11-02,NSW,Sydney,66062,15.3,22.6,0.5,5.6,9.9,ESE,31,20.1,56,21.8,46
2017-11-02,QLD,Brisbane,40913,14.7,27.6,0.6,10.2,11.9,E,46,19.4,51,27,35
2017-11-02,VIC,Melbourne,86338,11.3,17.4,1.4,,,SSE,26,12.7,79,15.9,56
2017-11-03,NSW,Sydney,66062,16.9,26.9,0.5,6.6,5.9,SSW,57,20.6,58,25.7,48
2017-11-03,QLD,Brisbane,40913,13.6,26.9,0.6,9.8,12.3,ESE,43,19.5,49,25.3,41
2017-11-03,VIC,Melbourne,86338,9.4,16.6,1.4,,,S,46,12,58,15.3,45
2017-11-04,NSW,Sydney,66062,16.2,17.1,1.6,7.4,0,SSE,39,17,73,15.8,78
2017-11-04,QLD,Brisbane,40913,14.8,30.7,0.6,8,12.6,E,69,23.1,43,30.3,20
2017-11-04,VIC,Melbourne,86338,8.3,15.8,0.4,,,SSW,35,10.2,72,14.5,47
2017-11-05,NSW,Sydney,66062,14.1,19.5,31.8,2.6,0,ENE,39,14.9,85,17.7,59
2017-11-05,QLD,Brisbane,40913,15.2,31.1,0.6,10.2,12.6,E,67,21.6,38,31,20
2017-11-05,VIC,Melbourne,86338,6.4,17.6,0,,,SSW,39,13.4,59,15.6,49
2017-11-06,NSW,Sydney,66062,14.8,26.3,4.4,1.8,5.7,SSW,67,19.4,81,23.6,57
2017-11-06,QLD,Brisbane,40913,14.9,32.7,0.6,10.2,12.5,E,67,23.5,33,32.7,11
2017-11-06,VIC,Melbourne,86338,10.9,16.1,0,,,S,43,13.8,66,14.8,60
2017-11-07,NSW,Sydney,66062,13.8,21.8,5.6,8.8,11.3,SSE,54,18.9,40,20,40
2017-11-07,QLD,Brisbane,40913,17.3,36.4,0.6,12,12.5,E,59,27.1,21,35.4,14
2017-11-07,VIC,Melbourne,86338,10,15.8,3.4,,,S,43,12.2,76,14.4,52
2017-11-08,NSW,Sydney,66062,14.6,21.7,0.5,9.6,10,SSE,44,16.8,54,20.5,44
2017-11-08,QLD,Brisbane,40913,20,35.6,0.6,11.2,10.8,ENE,37,28,30,33.1,24
2017-11-08,VIC,Melbourne,86338,12.1,16.9,0,,,S,30,13.9,59,15.6,64
2017-11-09,NSW,Sydney,66062,12.3,22.1,0.5,6.8,11.6,E,28,17.9,52,21.2,50
2017-11-09,QLD,Brisbane,40913,15.2,31.6,0.6,7.8,12.8,SW,37,26.9,42,29.7,31
2017-11-09,VIC,Melbourne,86338,8.7,21.8,0,,,SSW,28,14.6,68,18.1,66
2017-11-10,NSW,Sydney,66062,14.2,23.4,0.5,6.8,11.6,E,26,19.2,59,22.7,50
2017-11-10,QLD,Brisbane,40913,14.1,32,0.6,8.4,12.6,W,33,26.4,41,30.1,37
2017-11-10,VIC,Melbourne,86338,12.1,27.2,0,,,SE,20,15.7,78,22.8,63
2017-11-11,NSW,Sydney,66062,14.7,23.7,0.5,7.4,8.6,ENE,33,19.9,58,20.9,56
2017-11-11,QLD,Brisbane,40913,16.2,38,0.6,9,10,W,41,30.4,32,33.5,27
2017-11-11,VIC,Melbourne,86338,14.8,24.7,0,,,SSW,26,17.8,79,22,65
2017-11-12,NSW,Sydney,66062,17.1,22.5,0.5,7.4,10.7,E,31,19.7,49,21.7,47
2017-11-12,QLD,Brisbane,40913,22.4,32.1,0.6,11.8,11.3,S,39,28.7,39,28.8,46
2017-11-12,VIC,Melbourne,86338,13.2,23.3,0,,,SSW,28,16.1,86,21.6,69
2017-11-13,NSW,Sydney,66062,17.5,22.3,0.5,6.4,9.1,E,33,19.1,48,21.2,42
2017-11-13,QLD,Brisbane,40913,17.5,30.1,0.6,10.2,12.7,SW,50,25.2,65,27.9,45
2017-11-13,VIC,Melbourne,86338,13.7,33,0,,,NE,24,19.3,73,32,20
2017-11-14,NSW,Sydney,66062,16,22.9,0.5,7.6,8.9,ENE,37,20,53,22.3,46
2017-11-14,QLD,Brisbane,40913,14.5,29.7,0.6,11.8,12.7,SE,54,20.2,47,29.1,19
2017-11-14,VIC,Melbourne,86338,18.2,33.9,0,,,N,48,25.3,38,33.1,27

Trying to create a Planning algorithm

I have a list representing the year which is filled with a sub-list for each day of the year.
year = []
for i in range(0,52*7):
day = [i,0] #[day number, 0 = empty, 1 = something is planned]
year.append(day)
I also have a variable list of activities created by a class.
class Activities:
def __init__(self,name,weeks,weekends):
self.name = name
self.weeks = weeks
self.weekends = weekends
def __repr__(self):
return repr((self.name,self.weeks,self.weekends))
def activityMaker(activityList):
a= []
for i in range(0, len(activityList)):
a.append(Activities(activityList[i][0], activityList[i][1], activityList[i][2]))
a = sorted(a, key=lambda Activities: Activities.weeks)
activityList = a
return activityList
As an example;
>>> activityList = [['Tennis', 3, 0], ['Baseball', 4, 0], ['Swimming', 2, 0]]
>>>activities= activityMaker(activityList)
Which returns 'activities', sorted on Activities.weeks:
>>>activities[0].name
activities[0].week
activities[0].weekend
>>> 'Swimming' # activity name
2 #"i want to do this activity once every x weeks
0 # 0 = no preferance, 1 = not in weekends
Now here my dilemma. I wish to create an algorithm to fill year with the activities with as much rhythm as possible.
My current approach is not working properly. What I'm doing now is as follows.
for y in range(0,len(year), int(7*activities[0].weeks)):
year[y][1] = activities[i].name
Now the first activity is planned for each y. If I had two activities, which I each want planned once a week, I could plan the first on 0th, 7th, 14th etc, and the second on 3rd, 10th, 17th etcetera.
The problem with this approach is exemplified if activities[0] and activities[1] are respectively 2 and 3. If I apply the previous approach, activities[0] would be planned on the 0th, 14th, 28th etc, which is fine on its own. Between the 0th and 14th, the 2nd activity would be ideally placed on the 7th position, meaning the next time would be the 28th. On the 28 the first activity is already planned, however. This means that's there's nothing planned for two weeks and then suddenly 2 activities in a day. The second activity could be pushed to the 27th or 29th, but that would still mean that now activities are planned on the 0th, 7th, 14th, 28th, 29th. Aka, there's still 14 days between the 14th and the 28th, and then only 1 between the 28th and 29th.
In what way can I make sure that all activities are planned with as much average time in between activities?

Your problem is that unless the number of weeks is the same for all activities (so they all have the same rhythm, there will be some weeks with lots of activities and some weeks with none.
What I would suggest instead is this: As you walk through the weeks of the year, simply choose an activity (or two) at random for each week. That way, every week will have a modest amount of activity planned. Here's some example code:
import random
activities = ["Baseball", "Tennis", "Swimming", ... ]
skip_days = 3
year = {}
for y in range(0, 52*7, skip_days):
year[y] = random.choose(activities)
print year[0]
>>> "Swimming" (perhaps)
print year[15]
>>> "Baseball"
print year[17]
>>> None
If you want more activity, make skip_days smaller. If you want less, make it bigger. If you want a fixed amount of activity in each week, you could do something like
for y in range(0, 52*7, 7):
year[y] = random.choose(activities)
year[y+3] = random.choose(activities)
This would plan two days a week.

How to calculate combined experience

I have the following in a function:
edu = sorted(context.education, key=lambda studied: studied["studied_to"], reverse=True)
#print edu[0].studied_to
job_history = []
for job in context.job_history:
if edu[0].fields_of_study in job.industries:
from_ = job.from_
to_ = job.to_
industries = job.industries
rd = rdelta.relativedelta(to_, from_) # get date difference
# I suspect that combined exp calculation would be done here.
experience = "{0.years} Years {0.months} Months".format(rd) #get Year - Month format
#print experience
job_history.append({"job_title": job_title,
"company_name": company_name,
"from_": from_,
"to_": to_,
"industries": industries,
"experience": experience})
j = sorted(job_history, key=lambda s: s["to_"])
#print j[0]["job_title"]
return {"relocate_list": provinces,
"disabilities_dict": disabilities,
"industries_list": industry_dict,
"job_history_sorted": j,
"education_sorted": edu}
I can get the experience from each job with the code above. Is there a way to calculate the combined experience.
Currently, say the user has/had more than one job in the IT industry for arguments sake, The above code will give me e.g. 1 Years 0 Months and 1 Years 4 Months.
How can I calculate the combined experience so the above example would be 2 Years 4 Months?
I have tried:
rd += rd
But this adds the same date, i.e.
1 Years 4 Months + 1 Years 4 Months
would output:
2 Years 8 Months

Why not create a new variable to save the relative deltas and display it outside the loop, say:
job_history = []
total_exp = rdelta.relativedelta()
for job in context.job_history:
if edu[0].fields_of_study in job.industries:
from_ = job.from_
to_ = job.to_
industries = job.industries
rd = rdelta.relativedelta(to_, from_) # get date difference
total_exp += rd
job_history.append({"job_title": job_title,
"company_name": company_name,
"from_": from_,
"to_": to_,
"industries": industries,
"experience": experience})
# I suspect that combined exp calculation would be done here.
experience = "{0.years} Years {0.months} Months".format(total_exp) #get Year - Month format
#print experience

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