Python读取数据的文本文件，然后从字符串列表中提取子字符串

Question

I have a weather data file that has high temps, low temp, rainfall, etc. I need to open the file and return data based on year ranges from user input. User inputs a starting date and ending date then I put that data into a list that user can then search for highest (HIGHTEMP) or lowest temps (LOWTEMP) or highest rainfall (PRCP) in that sub-list of data of year ranges. Currently I can search for strings, but not sure how to identify the high temps, for example, then gather the high temps in the sub-list, then find the highest, then return that data. Same with low temp and rain fall.

Here is what I have so far:

def openFile():
    begin = input("Enter your starting year in this format YYYY ")
    end = input("Enter your ending year for weather data in this format YYYY ")

    lines = tuple(open('/Users/jasontt/test/spokaneweatherdata.txt', 'r'))
    #print(lines)
    print("")
    #print(lines[1])
    print("")

    result = [i for i in lines if str(begin) in i]
    #print("This is begining data ", result)

    resultTwo = [i for i in lines if str(end) in i]
    #print("This is end of data ", resultTwo)
    #Combined list based on years entered
    ultimateList = [result + resultTwo]
    #Combined list of weather data for years selected
    print(ultimateList)

    '''

Test Data:

STATION           STATION_NAME                                       ELEVATION  LATITUDE   LONGITUDE  DATE     PRCP     TEMPMAX     TEMPMIN
----------------- -------------------------------------------------- ---------- ---------- ---------- -------- -------- -------- --------
GHCND:USW00013741                     SPOKANE REGIONAL AIRPORT WA US      366.1   37.31667  -79.96667 19490101 0.00     44       27
GHCND:USW00013741                     SPOKANE REGIONAL AIRPORT WA US      366.1   37.31667  -79.96667 19490102 0.00     42       25
GHCND:USW00013741                     SPOKANE REGIONAL AIRPORT WA US      366.1   37.31667  -79.96667 19490103 0.15     46       30
GHCND:USW00013741                     SPOKANE REGIONAL AIRPORT WA US      366.1   37.31667  -79.96667 19490104 0.03     41       30
GHCND:USW00013741                     SPOKANE REGIONAL AIRPORT WA US      366.1   37.31667  -79.96667 19490105 1.14     46       37
GHCND:USW00013741                     SPOKANE REGIONAL AIRPORT WA US      366.1   37.31667  -79.96667 19490106 0.00     51       40
GHCND:USW00013741                     SPOKANE REGIONAL AIRPORT WA US      366.1   37.31667  -79.96667 19490107 0.00     57       36
GHCND:USW00013741                     SPOKANE REGIONAL AIRPORT WA US      366.1   37.31667  -79.96667 19490108 0.00     56       45
GHCND:USW00013741                     SPOKANE REGIONAL AIRPORT WA US      366.1   37.31667  -79.96667 19490109 0.00     66       42
GHCND:USW00013741                     SPOKANE REGIONAL AIRPORT WA US      366.1   37.31667  -79.96667 19490110 0.00     70       51
GHCND:USW00013741                     SPOKANE REGIONAL AIRPORT WA US      366.1   37.31667  -79.96667 19490111 0.03     59       45
GHCND:USW00013741                     SPOKANE REGIONAL AIRPORT WA US      366.1   37.31667  -79.96667 19490112 0.04     48       38
GHCND:USW00013741                     SPOKANE REGIONAL AIRPORT WA US      366.1   37.31667  -79.96667 19490113 0.00     52       36
GHCND:USW00013741                     SPOKANE REGIONAL AIRPORT WA US      366.1   37.31667  -79.96667 19490114 0.00     56       36
GHCND:USW00013741                     SPOKANE REGIONAL AIRPORT WA US      366.1   37.31667  -79.96667 19490115 0.00     49       31
GHCND:USW00013741                     SPOKANE REGIONAL AIRPORT WA US      366.1   37.31667  -79.96667 19490116 0.00     68       28
GHCND:USW00013741                     SPOKANE REGIONAL AIRPORT WA US      366.1   37.31667  -79.96667 19490117 0.00     63       50
GHCND:USW00013741                     SPOKANE REGIONAL AIRPORT WA US      366.1   37.31667  -79.96667 19490118 0.04     53       42
GHCND:USW00013741                     SPOKANE REGIONAL AIRPORT WA US      366.1   37.31667  -79.96667 19490119 0.01     63       38
GHCND:USW00013741                     SPOKANE REGIONAL AIRPORT WA US      366.1   37.31667  -79.96667 19490120 0.00     45       28
GHCND:USW00013741                     SPOKANE REGIONAL AIRPORT WA US      366.1   37.31667  -79.96667 19490121 0.97     35       28
GHCND:USW00013741                     SPOKANE REGIONAL AIRPORT WA US      366.1   37.31667  -79.96667 19490122 0.29     60       34
GHCND:USW00013741                     SPOKANE REGIONAL AIRPORT WA US      366.1   37.31667  -79.96667 19490123 0.14     47       38
GHCND:USW00013741                     SPOKANE REGIONAL AIRPORT WA US      366.1   37.31667  -79.96667 19490124 0.01     72       38
GHCND:USW00013741                     SPOKANE REGIONAL AIRPORT WA US      366.1   37.31667  -79.96667 19490125 0.05     66       49

Answer 1

It's difficult to tell from a copy-pasted data sample, but it looks like your file is using a "fixed-width" line format - each column in a line starts at a given position and ends at a given position. This was a quite common type of "format" by the days...

So what you want here is to write down each columns name, start and end position, so you can easily parse the lines into fields, ie:

FORMAT_MAP = {
    # fieldname : (start, end)
    "STATION": (0, 17),
    "STATION_NAME": (18, 68),
    "ELEVATION": (69, 79),
    # etc...
    }


def parse_line(line):
    return {name: line[start:end].strip() for name, (start, end) in FORMAT_MAP.items()}

Now you can parse your file into a sequence of fields dicts:

def iter_parse_file(f, startyear, endyear):
   # skip the first two header lines
   next(f);  next(f)

   for line in f: 
      # we assume the lines are sorted on date, and that the
      # date format is YYYYMMDD. 
      row = parse_line(line)
      year = row["DATE"][:4]
      if year < startyear:
         continue
      elif year > endyear:
         break
      yield row


with open("your/file.ext") as f:
    rows = list(iter_parse_file(f, startyear, endyear))

for row in rows:
    print("{DATE} : {TEMPMIN} - {TEMPMAX}".format(**row))

you can also filter, sort etc on columns values, build a panda dataframe etc.

Note that you can (and probably want to) convert your data to the proper type during parsing. With the above starting point you should be able to do so quite easily.