Is there a way to specify a DataFrame index (row) based on matching text inside the dataframe?
I am importing a text file from the internet located here every day into a python pandas DataFrame. I am parsing out just some of the data and doing calculations to give me the peak value for each day. The specific group of data I am needing to gather starts with the section headed "RTO COMBINED HOUR ENDING INTEGRATED FORECAST LOAD MW".
I need to specifically only use part of the data to do the calculations I need and I am able to manually specify which index line to start with, but daily this number could change due to text added to the top of the file by the authors.
Updated as of: 05-05-2016 1700 Constrained operations ARE expected in the AEP, APS, BC, COMED, DOM,and PS zones on 05-06-2016. Constrained operations ARE expected in the AEP, APS, BC, COMED, DOM,and PS zones on 05-07-2016. The PS/ConEd 600/400 MW contract will be limited to 700MW on 05-06-16.
Is there a way to match text in the pandas DataFrame and specify the index of that match? Currently I am manually specifying the index I want to start with using the variable 'day' below on the 6th line. I would like this variable to hold the index (row) of the dataframe that includes the text I want to match.
The code below works but may stop working if the line number (index) changes:
def forecastload():
wb = load_workbook(filename = 'pjmactualload.xlsx')
ws = wb['PJM Load']
printRow = 13
#put this in iteration to pull 2 rows of data at a time (one for each day) for 7 days max
day = 239
while day < 251:
#pulls in first day only
data = pd.read_csv("http://oasis.pjm.com/doc/projload.txt", skiprows=day, delim_whitespace=True, header=None, nrows=2)
#sets data at HE 24 = to data that is in HE 13- so I can delete column 0 data to allow checking 'max'
data.at[1,13]= data.at[1,1]
#get date for printing it with max load later on
newDate = str(data.at[0,0])
#now delete first column to get rid of date data. date already saved as newDate
data = data.drop(0,1)
data = data.drop(1,1)
#pull out max value of day
#add index to this for iteration ie dayMax[x] = data.values.max()
dayMax = data.max().max()
dayMin = data.min().min()
#print date and max load for that date
actualMax = "Forecast Max"
actualMin = "Forecast Min"
dayMax = int(dayMax)
maxResults = [str(newDate),int(dayMax),actualMax,dayMin,actualMin]
d = 1
for items in maxResults:
ws.cell(row=printRow, column=d).value = items
d += 1
printRow += 1
#print maxResults
#l.writerows(maxResults)
day = day + 2
wb.save('pjmactualload.xlsx')
In this case i recommend you to use the command line in order to obtain a dataset that you could read later with pandas and do whatever you want.
To retrieve the data you can use curl and grep:
$ curl -s http://oasis.pjm.com/doc/projload.txt | grep -A 17 "RTO COMBINED HOUR ENDING INTEGRATED FORECAST" | tail -n +5
05/06/16 am 68640 66576 65295 65170 66106 70770 77926 83048 84949 85756 86131 86089
pm 85418 85285 84579 83762 83562 83289 82451 82460 84009 82771 78420 73258
05/07/16 am 66809 63994 62420 61640 61848 63403 65736 68489 71850 74183 75403 75529
pm 75186 74613 74072 73950 74386 74978 75135 75585 77414 76451 72529 67957
05/08/16 am 63583 60903 59317 58492 58421 59378 60780 62971 66289 68997 70436 71212
pm 71774 71841 71635 71831 72605 73876 74619 75848 78338 77121 72665 67763
05/09/16 am 63865 61729 60669 60651 62175 66796 74620 79930 81978 83140 84307 84778
pm 85112 85562 85568 85484 85766 85924 85487 85737 87366 84987 78666 72166
05/10/16 am 67581 64686 62968 62364 63400 67603 75311 80515 82655 84252 86078 87120
pm 88021 88990 89311 89477 89752 89860 89256 89327 90469 87730 81220 74449
05/11/16 am 70367 67044 65125 64265 65054 69060 76424 81785 84646 87097 89541 91276
pm 92646 93906 94593 94970 95321 95073 93897 93162 93615 90974 84335 77172
05/12/16 am 71345 67840 65837 64892 65600 69547 76853 82077 84796 87053 89135 90527
pm 91495 92351 92583 92473 92541 92053 90818 90241 90750 88135 81816 75042
Let's use the previous output (in the rto.txt file) to obtain a more readable data using awk and sed:
$ awk '/^ [0-9]/{d=$1;print $0;next}{print d,$0}' rto.txt | sed 's/^ //;s/\s\+/,/g'
05/06/16,am,68640,66576,65295,65170,66106,70770,77926,83048,84949,85756,86131,86089
05/06/16,pm,85418,85285,84579,83762,83562,83289,82451,82460,84009,82771,78420,73258
05/07/16,am,66809,63994,62420,61640,61848,63403,65736,68489,71850,74183,75403,75529
05/07/16,pm,75186,74613,74072,73950,74386,74978,75135,75585,77414,76451,72529,67957
05/08/16,am,63583,60903,59317,58492,58421,59378,60780,62971,66289,68997,70436,71212
05/08/16,pm,71774,71841,71635,71831,72605,73876,74619,75848,78338,77121,72665,67763
05/09/16,am,63865,61729,60669,60651,62175,66796,74620,79930,81978,83140,84307,84778
05/09/16,pm,85112,85562,85568,85484,85766,85924,85487,85737,87366,84987,78666,72166
05/10/16,am,67581,64686,62968,62364,63400,67603,75311,80515,82655,84252,86078,87120
05/10/16,pm,88021,88990,89311,89477,89752,89860,89256,89327,90469,87730,81220,74449
05/11/16,am,70367,67044,65125,64265,65054,69060,76424,81785,84646,87097,89541,91276
05/11/16,pm,92646,93906,94593,94970,95321,95073,93897,93162,93615,90974,84335,77172
05/12/16,am,71345,67840,65837,64892,65600,69547,76853,82077,84796,87053,89135,90527
05/12/16,pm,91495,92351,92583,92473,92541,92053,90818,90241,90750,88135,81816,75042
now, read and reshape the above result with pandas:
df = pd.read_csv("rto2.txt",names=["date","period"]+list(range(1,13)),index_col=[0,1])
df = df.stack().reset_index().rename(columns={"level_2":"hour",0:"value"})
df.index = pd.to_datetime(df.apply(lambda x: "{date} {hour}:00 {period}".format(**x),axis=1))
df.drop(["date", "hour", "period"], axis=1, inplace=True)
At this point you have a beautiful time series :)
In [10]: df.head()
Out[10]:
value
2016-05-06 01:00:00 68640
2016-05-06 02:00:00 66576
2016-05-06 03:00:00 65295
2016-05-06 04:00:00 65170
2016-05-06 05:00:00 66106
to obtain the statistics:
In[11]: df.groupby(df.index.date).agg([min,max])
Out[11]:
value
min max
2016-05-06 65170 86131
2016-05-07 61640 77414
2016-05-08 58421 78338
2016-05-09 60651 87366
2016-05-10 62364 90469
2016-05-11 64265 95321
2016-05-12 64892 92583
I hope this can help you.
Regards.
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