Python: How to retrieve the count of values in a column reading a CSV? [closed]

I have a given weather data and i want to read it in python. The sample is given as below:

Sample

OB_END_TIME,ID,ID_TYPE,OB_HOUR_COUNT,VERSION_NUM,MET_DOMAIN_NAME,SRC_ID,REC_ST_IND,PRCP_AMT,PRCP_DUR,PRCP_AMT_Q,PRCP_DUR_Q,METO_STMP_TIME,MIDAS_STMP_ETIME,PRCP_AMT_J
2016-01-01 00:00, 001586, RAIN, 1, 1, SREW, 315, 1011, 0, , 1, , 2015-12-31 23:54, 0, ,
2016-01-01 00:00, 005349, RAIN, 1, 1, AWSHRLY, 310, 1011, 0, , 1, , 2016-01-01 00:00, 0, ,
2016-01-01 00:00, 008888, RAIN, 1, 1, AWSHRLY, 289, 1011, 0, , 1, , 2016-01-01 02:04, 0, ,
2016-01-01 00:00, 009182, CLBR, 1, 1, AWSHRLY, 57254, 1011, 0, , 1, , 2016-01-01 04:20, 0, ,
2016-01-01 00:00, 010990, RAIN, 1, 1, SREW, 16589, 1011, 0, , 1, , 2015-12-31 23:52, 0, ,
2016-01-01 00:00, 017221, RAIN, 1, 1, SREW, 30523, 1011, 0, , 1, , 2015-12-31 23:54, 0, ,
2016-01-01 00:00, 024726, RAIN, 1, 1, AWSHRLY, 326, 1011, 0, , 1, , 2016-01-01 02:04, 0, ,
2016-01-01 00:00, 034592, RAIN, 1, 1, SREW, 17344, 1011, 0, , 1, , 2015-12-31 23:54, 0, ,
2016-01-01 00:00, 036375, RAIN, 1, 1, SREW, 358, 1011, 0, , 1, , 2015-12-31 23:53, 0, ,
2016-01-01 00:00, 037240, RAIN, 1, 1, AWSHRLY, 360, 1011, 0, , 1, , 2015-12-31 23:53, 0, ,
2016-01-01 00:00, 038131, RAIN, 1, 1, SREW, 373, 1011, 0, , 1, , 2015-12-31 23:54, 0, ,
2016-01-01 00:00, 038180, RAIN, 1, 1, AWSHRLY, 367, 1011, 0, , 1, , 2016-01-01 00:00, 0, ,
2016-01-01 00:00, 042647, RAIN, 1, 1, AWSHRLY, 56986, 1011, 0, , 1, , 2016-01-01 00:00, 0, ,
2016-01-01 00:00, 044287, RAIN, 1, 1, SREW, 370, 1011, 0, , 1, , 2015-12-31 23:52, 0, ,
2016-01-01 00:00, 047714, RAIN, 1, 1, AWSHRLY, 339, 1011, 0, , 1, , 2016-01-01 00:00, 0, ,
2016-01-01 00:00, 052426, RAIN, 1, 1, AWSHRLY, 30750, 1011, 0, , 1, , 2016-01-01 01:59, 0, ,
2016-01-01 00:00, 053903, RAIN, 1, 1, SREW, 17314, 1011, 0, , 1, , 2016-01-01 01:36, 0, ,
2016-01-01 00:00, 056005, RAIN, 1, 1, SREW, 16596, 1011, 0, , 1, , 2015-12-31 23:51, 0, ,
2016-01-01 00:00, 056349, RAIN, 1, 1, SREW, 342, 1011, 0, , 1, , 2016-01-01 01:49, 0, ,
2016-01-01 00:00, 057294, RAIN, 1, 1, SREW, 346, 1011, 0, , 1, , 2016-01-01 01:36, 0, ,
2016-01-01 00:00, 057936, RAIN, 1, 1, AWSHRLY, 25726, 1011, 0, , 1, , 2016-01-01 02:04, 0, ,
2016-01-01 00:00, 064286, RAIN, 1, 1, AWSHRLY, 534, 1011, 0, , 1, , 2015-12-31 23:55, 0, ,
2016-01-01 00:00, 075266, RAIN, 1, 1, SREW, 513, 1011, 0, , 10001, , 2015-12-31 23:54, 0, ,
2016-01-01 00:00, 075633, RAIN, 1, 1, AWSHRLY, 516, 1011, 0, , 1, , 2016-01-01 02:02, 0, ,
2016-01-01 00:00, 082584, RAIN, 1, 1, AWSHRLY, 525, 1011, 0, , 1, , 2016-01-01 02:05, 0, ,
2016-01-01 00:00, 085160, RAIN, 1, 1, SREW, 56958, 1011, 0, , 1, , 2015-12-31 23:54, 0, ,
2016-01-01 00:00, 087398, RAIN, 1, 1, AWSHRLY, 529, 1011, 0, , 1, , 2016-01-01 02:00, 0, ,
2016-01-01 00:00, 089543, RAIN, 1, 1, AWSHRLY, 622, 1011, 0, , 1, , 2015-12-31 23:57, 0, ,
2016-01-01 00:00, 095456, RAIN, 1, 1, AWSHRLY, 56424, 1011, 0, , 1, , 2015-12-31 23:58, 0, ,
2016-01-01 00:00, 096514, RAIN, 1, 1, SREW, 19187, 1011, 0, , 1, , 2015-12-31 23:52, 0, ,
2016-01-01 00:00, 099173, RAIN, 1, 1, AWSHRLY, 30529, 1011, 0, , 1, , 2015-12-31 23:59, 0, ,
2016-01-01 00:00, 100789, RAIN, 1, 1, AWSHRLY, 542, 1011, 0, , 1, , 2015-12-31 23:59, 0, ,
2016-01-01 00:00, 102844, RAIN, 1, 1, SREW, 30690, 1011, 0, , 1, , 2015-12-31 23:56, 0, ,
2016-01-01 00:00, 107737, CLBR, 1, 1, AWSHRLY, 61973, 1011, 0, , 1, , 2016-01-01 09:38, 13, ,
2016-01-01 00:00, 115901, RAIN, 1, 1, AWSHRLY, 554, 1011, 0, , 1, , 2016-01-01 02:06, 0, ,
2016-01-01 00:00, 117629, RAIN, 1, 1, SREW, 556, 1011, 0, , 1, , 2015-12-31 23:51, 0, ,
2016-01-01 00:00, 124664, RAIN, 1, 1, AWSHRLY, 19204, 1011, 0, , 1, , 2016-01-01 00:00, 0, ,
2016-01-01 00:00, 130062, RAIN, 1, 1, AWSHRLY, 382, 1011, 0, , 1, , 2016-01-01 00:00, 0, ,
2016-01-01 00:00, 138518, RAIN, 1, 1, SREW, 16725, 1011, 0, , 1, , 2015-12-31 23:52, 0, ,
2016-01-01 00:00, 142002, RAIN, 1, 1, SREW, 384, 1011, 0, , 1, , 2015-12-31 23:54, 0, ,
2016-01-01 00:00, 142508, RAIN, 1, 1, SREW, 381, 1011, 0, , 1, , 2015-12-31 23:51, 0, ,
2016-01-01 00:00, 146128, RAIN, 1, 1, SREW, 393, 1011, 0, , 1, , 2015-12-31 23:54, 0, ,
2016-01-01 00:00, 146453, RAIN, 1, 1, SREW, 386, 1011, 0, , 1, , 2016-01-01 01:36, 0, ,
2016-01-01 00:00, 157291, RAIN, 1, 1, SREW, 395, 1011, 0, , 1, , 2015-12-31 23:53, 0, ,
2016-01-01 00:00, 160108, RAIN, 1, 1, AWSHRLY, 578, 1011, 0, , 1, , 2016-01-01 02:05, 0, ,
2016-01-01 00:00, 164016, RAIN, 1, 1, SREW, 583, 1011, 0, , 1, , 2015-12-31 23:51, 0, ,
2016-01-01 00:00, 169350, RAIN, 1, 1, AWSHRLY, 57093, 1011, 0, , 1, , 2016-01-01 02:06, 0, ,
2016-01-01 00:00, 172604, RAIN, 1, 1, AWSHRLY, 458, 1011, 0, , 1, , 2015-12-31 23:53, 0, ,
2016-01-01 00:00, 174063, RAIN, 1, 1, SREW, 461, 1011, 0, , 1, , 2015-12-31 23:53, 0, ,
2016-01-01 00:00, 183798, RAIN, 1, 1, AWSHRLY, 455, 1011, 0, , 1, , 2015-12-31 23:59, 0, ,
2016-01-01 00:00, 186331, RAIN, 1, 1, AWSHRLY, 435, 1011, 0, , 1, , 2016-01-01 00:00, 0, ,
2016-01-01 00:00, 190541, RAIN, 1, 1, AWSHRLY, 413, 1011, 0, , 1, , 2016-01-01 02:00, 0, ,
2016-01-01 00:00, 193361, RAIN, 1, 1, SREW, 409, 1011, 0, , 1, , 2016-01-01 01:36, 0, ,
2016-01-01 00:00, 196391, RAIN, 1, 1, AWSHRLY, 456, 1011, 0, , 1, , 2016-01-01 02:06, 0, ,
2016-01-01 00:00, 200640, CLBR, 1, 1, AWSHRLY, 61843, 1011, , , 7000, , 2016-01-01 09:38, 13, ,
2016-01-01 00:00, 201424, RAIN, 1, 1, AWSHRLY, 30476, 1011, 0, , 1, , 2015-12-31 23:59, 0, ,
2016-01-01 00:00, 203770, RAIN, 1, 1, SREW, 421, 1011, 0, , 1, , 2015-12-31 23:59, 0, ,
2016-01-01 00:00, 203879, RAIN, 1, 1, AWSHRLY, 426, 1011, 0, , 1, , 2015-12-31 23:59, 0, ,
2016-01-01 00:00, 206102, RAIN, 1, 1, AWSHRLY, 61986, 1011, 0, , 1, , 2016-01-01 02:04, 0, ,
2016-01-01 00:00, 214288, RAIN, 1, 1, AWSHRLY, 4911, 1011, 0, , 1, , 2016-01-01 00:00, 0, ,
2016-01-01 00:00, 214511, CLBR, 1, 1, AWSHRLY, 61915, 1011, 0, , 1, , 2016-01-01 09:38, 13, ,
2016-01-01 00:00, 220392, RAIN, 1, 1, AWSHRLY, 442, 1011, 0, , 1, , 2016-01-01 02:01, 0, ,
2016-01-01 00:00, 221993, RAIN, 1, 1, SREW, 440, 1011, 0, , 1, , 2016-01-01 01:49, 0, ,
2016-01-01 00:00, 224244, RAIN, 1, 1, AWSHRLY, 436, 1011, 0, , 1, , 2016-01-01 00:00, 0, ,
2016-01-01 00:00, 232673, RAIN, 1, 1, AWSHRLY, 487, 1011, 0, , 1, , 2015-12-31 23:59, 0, ,
2016-01-01 00:00, 233754, RAIN, 1, 1, SREW, 19188, 1011, 0, , 1, , 2015-12-31 23:54, 0, ,
2016-01-01 00:00, 236429, RAIN, 1, 1, SREW, 498, 1011, 0, , 1, , 2015-12-31 23:52, 0, ,
2016-01-01 00:00, 239258, RAIN, 1, 1, AWSHRLY, 481, 1011, 0, , 1, , 2015-12-31 23:58, 0, ,
2016-01-01 00:00, 246262, RAIN, 1, 1, SREW, 697, 1011, 0, , 1, , 2015-12-31 23:52, 0, ,
2016-01-01 00:00, 246691, RAIN, 1, 1, AWSHRLY, 695, 1011, 0, , 1, , 2016-01-01 02:04, 0, ,
2016-01-01 00:00, 247345, RAIN, 1, 1, SREW, 709, 1011, 0, , 1, , 2016-01-01 01:49, 0, ,
2016-01-01 00:00, 247539, RAIN, 1, 1, SREW, 708, 1011, 0, , 1, , 2015-12-31 23:52, 0, ,
2016-01-01 00:00, 252450, RAIN, 1, 1, SREW, 605, 1011, 0, , 1, , 2015-12-31 23:54, 0, ,
2016-01-01 00:00, 253699, RAIN, 1, 1, SREW, 692, 1011, 0, , 1, , 2015-12-31 23:51, 0, ,
2016-01-01 00:00, 257863, RAIN, 1, 1, AWSHRLY, 607, 1011, 0, , 1, , 2016-01-01 02:05, 0, ,
2016-01-01 00:00, 264250, RAIN, 1, 1, SREW, 613, 1011, 0, , 1, , 2016-01-01 01:49, 0, ,
2016-01-01 00:00, 268197, RAIN, 1, 1, AWSHRLY, 19159, 1011, 0, , 1, , 2016-01-01 02:05, 0, ,
2016-01-01 00:00, 270401, RAIN, 1, 1, AWSHRLY, 830, 1011, 0, , 1, , 2015-12-31 23:58, 0, ,
2016-01-01 00:00, 271418, RAIN, 1, 1, SREW, 869, 1011, 0, , 1, , 2015-12-31 23:52, 0, ,
2016-01-01 00:00, 271976, RAIN, 1, 1, SREW, 862, 1011, 0, , 1, , 2016-01-01 01:49, 0, ,
2016-01-01 00:00, 274319, RAIN, 1, 1, SREW, 17176, 1011, 0, , 1, , 2015-12-31 23:52, 0, ,
2016-01-01 00:00, 276541, RAIN, 1, 1, SREW, 471, 1011, 0, , 1, , 2015-12-31 23:53, 0, ,
2016-01-01 00:00, 280840, RAIN, 1, 1, AWSHRLY, 868, 1011, 0, , 1, , 2016-01-01 02:07, 0, ,
2016-01-01 00:00, 283425, RAIN, 1, 1, AWSHRLY, 719, 1011, 0, , 1, , 2016-01-01 02:06, 0, ,
2016-01-01 00:00, 284153, RAIN, 1, 1, AWSHRLY, 711, 1011, 0, , 1, , 2016-01-01 02:02, 0, ,
2016-01-01 00:00, 284337, RAIN, 1, 1, SREW, 30620, 1011, 0, , 1, , 2015-12-31 23:52, 0, ,
2016-01-01 00:00, 287051, RAIN, 1, 1, AWSHRLY, 723, 1011, 0, , 1, , 2016-01-01 02:06, 0, ,
2016-01-01 00:00, 287675, RAIN, 1, 1, SREW, 726, 1011, 0, , 1, , 2015-12-31 23:51, 0, ,
2016-01-01 00:00, 292819, RAIN, 1, 1, AWSHRLY, 23450, 1011, 0, , 1, , 2016-01-01 00:00, 0, ,
2016-01-01 00:00, 295607, RAIN, 1, 1, AWSHRLY, 742, 1011, 0, , 1, , 2016-01-01 02:04, 0, ,
2016-01-01 00:00, 296649, RAIN, 1, 1, AWSHRLY, 56904, 1011, 0, , 1, , 2016-01-01 02:10, 0, ,
2016-01-01 00:00, 297881, RAIN, 1, 1, AWSHRLY, 744, 1011, 0, , 1, , 2016-01-01 02:06, 0, ,
2016-01-01 00:00, 301095, RAIN, 1, 1, SREW, 775, 1011, 0, , 1, , 2015-12-31 23:53, 0, ,
2016-01-01 00:00, 309753, RAIN, 1, 1, SREW, 811, 1011, 0, , 1, , 2015-12-31 23:52, 0, ,
2016-01-01 00:00, 315635, RAIN, 1, 1, SREW, 795, 1011, 0, , 1, , 2015-12-31 23:53, 0, ,
2016-01-01 00:00, 319170, RAIN, 1, 1, AWSHRLY, 16769, 1011, 0, , 1, , 2016-01-01 02:04, 0, ,
2016-01-01 00:00, 321374, RAIN, 1, 1, SREW, 779, 1011, 0, , 1, , 2015-12-31 23:53, 0, ,
2016-01-01 00:00, 325741, RAIN, 1, 1, AWSHRLY, 855, 1011, 0, , 1, , 2016-01-01 02:04, 0, ,
2016-01-01 00:00, 329463, RAIN, 1, 1, SREW, 847, 1011, 0, , 1, , 2016-01-01 01:36, 0, ,
2016-01-01 00:00, 331630, CLBR, 1, 1, AWSHRLY, 57247, 1011, 0, , 1, , 2016-01-01 09:38, 13, ,
2016-01-01 00:00, 333959, RAIN, 1, 1, SREW, 876, 1011, 0, , 1, , 2015-12-31 23:56, 0, ,
2016-01-01 00:00, 336378, RAIN, 1, 1, SREW, 889, 1011, 0, , 1, , 2016-01-01 01:49, 0, ,
2016-01-01 00:00, 336403, RAIN, 1, 1, SREW, 888, 1011, 0, , 1, , 2016-01-01 01:36, 0, ,
2016-01-01 00:00, 340767, CLBR, 1, 1, AWSHRLY, 61846, 1011, 0, , 1, , 2016-01-01 09:38, 13, ,
2016-01-01 00:00, 346476, RAIN, 1, 1, SREW, 842, 1011, 0, , 1, , 2015-12-31 23:51, 0, ,
2016-01-01 00:00, 350280, RAIN, 1, 1, AWSHRLY, 1326, 1011, 0, , 1, , 2016-01-01 02:07, 0, ,
2016-01-01 00:00, 350777, RAIN, 1, 1, SREW, 1319, 1011, 0, , 1, , 2015-12-31 23:55, 0, ,
2016-01-01 00:00, 355365, RAIN, 1, 1, SREW, 1378, 1011, 0, , 1, , 2015-12-31 23:54, 0, ,
2016-01-01 00:00, 356452, RAIN, 1, 1, SREW, 1285, 1011, 0, , 1, , 2015-12-31 23:56, 0, ,
2016-01-01 00:00, 358327, RAIN, 1, 1, SREW, 1383, 1011, 0, , 1, , 2015-12-31 23:51, 0, ,
2016-01-01 00:00, 362189, CLBR, 1, 1, AWSHRLY, 61938, 1011, 0, , 1, , 2016-01-01 09:38, 13, ,
2016-01-01 00:00, 368490, RAIN, 1, 1, SREW, 1336, 1011, 0, , 1, , 2015-12-31 23:56, 0, ,
2016-01-01 00:00, 376660, RAIN, 1, 1, SREW, 1415, 1011, 0, , 1, , 2015-12-31 23:52, 0, ,

The data given is very big and updates hourly. that is OB_END_TIME variable updates hourly. I want to get a count of unique SRC_ID corresponding to each hour of OB_END_TIME. that is the output should be like:

OUTPUT

OB_END_TIME_HOUR  SRC_ID  FREQUENCY
   00:00           315       1
   00:00           310       1

and so on. The data also have more values but i cannot share it due to restrictions. The OB_END_TIME variable also consist of data with more values like 2016-01-01 01:00, 2016-01-01 02:00 etc. i have been through this problem from yesterday and cannot find a clue on how to work out for this solution. The main problem is i cannot install Pandas and Numpy due to system errors so i have to carry it out in a generic and base python way. I hope i am clear with the problem. more issues can be resolved in comments. I have just imported the CSV in the python code and i am clueless on what to do next. So i think providing a code won't help anybody.


Asked by: Anna995 | Posted: 27-01-2022






Answer 1

You could use only modules from the standard python library: csv and collections

A csv reader is enough to process the csv file line by line and get access to fields. So you can easily read the OB_END_TIME and SRC_ID fields on each line - getting the hour is just a matter of taking a substring

What you have to do then is to feed a dictionnary with the hours as keys and having values of dictionnaries (again) with src_id as keys and count as values. That way you can then access counts as simply as count[hour][src_id]

The magic comes with collections.defaultdict which can automatically create a new value when you ask a non-existent key. Code could be:

import csv, collections

# use a defaultdict of defaultdicts with default value 0
dd = collections.defaultdict((lambda: collections.defaultdict(int)))
with open('filename.csv') as fd:
    rd = csv.reader(fd)  # a mere cvs reader (could also be a DictReader)
    next(rd) # skip header line (not for a DictReader)
    for row in rd:
        hour = row[0][11:]  # get relevant values
        src_id = row[6]
        dd[hour][src_id] += 1  # simply count, thanks to defaultdict magic

You can then process the dict with:

for hour in sorted(dd.keys()):
    for src_id in sorted(dd[hour].keys()):
        print hour, src_id, dd[hour][src_id]

Answered by: Emily983 | Posted: 28-02-2022



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