文件python中年和月数据的平均温度

Question

I have a data file with a data in some specific format and has some extra lines to ignore while processing. I need to process the data and calculate a value based on the same.

Sample Data:

Average monthly temperatures in Dubuque, Iowa, 
January 1964 through december 1975, n=144

24.7    25.7    30.6    47.5    62.9    68.5    73.7    67.9    61.1    48.5    39.6    20.0
16.1    19.1    24.2    45.4    61.3    66.5    72.1    68.4    60.2    50.9    37.4    31.1
10.4    21.6    37.4    44.7    53.2    68.0    73.7    68.2    60.7    50.2    37.2    24.6
21.5    14.7    35.0    48.3    54.0    68.2    69.6    65.7    60.8    49.1    33.2    26.0
19.1    20.6    40.2    50.0    55.3    67.7    70.7    70.3    60.6    50.7    35.8    20.7
14.0    24.1    29.4    46.6    58.6    62.2    72.1    71.7    61.9    47.6    34.2    20.4
8.4     19.0    31.4    48.7    61.6    68.1    72.2    70.6    62.5    52.7    36.7    23.8
11.2    20.0    29.6    47.7    55.8    73.2    68.0    67.1    64.9    57.1    37.6    27.7
13.4    17.2    30.8    43.7    62.3    66.4    70.2    71.6    62.1    46.0    32.7    17.3
22.5    25.7    42.3    45.2    55.5    68.9    72.3    72.3    62.5    55.6    38.0    20.4
17.6    20.5    34.2    49.2    54.8    63.8    74.0    67.1    57.7    50.8    36.8    25.5
20.4    19.6    24.6    41.3    61.8    68.5    72.0    71.1    57.3    52.5    40.6    26.2

Source of Sample File: http://robjhyndman.com/tsdldata/data/cryer2.dat

Note: Here, rows represent the years and columns represent the months.

I am trying to write a function which returns the average temperature of any month from the given url.

I have tried as below:

def avg_temp_march(f):

   march_temps = []

    # read each line of the file and store the values
    # as floats in a list
    for line in f:
        line = str(line, 'ascii') # now line is a string
        temps = line.split()
    # check that it is not empty.
        if temps != []:
            march_temps.append(float(temps[2]))

    # calculate the average and return it
    return sum(march_temps) / len(march_temps)

avg_temp_march("data5.txt")

But I am getting the error line = str(line, 'ascii')

TypeError: decoding str is not supported

Answer 1

I think there is no requirement for converting a string to string.

I tried to fix your code with some modifications:

def avg_temp_march(f):
    # f is a string read from file

    march_temps = []

    for line in f.split("\n"):
        if line == "":  continue
        temps = line.split(" ")
        temps = [t for t in temps if t != ""]

        # check that it is not empty.
        month_index = 2
        if len(temps) > month_index:

            try:
                march_temps.append(float(temps[month_index]))
            except Exception, e:
                print temps
                print "Skipping line:", e
    # calculate the average and return it
    return sum(march_temps) / len(march_temps)

Output:

['Average', 'monthly', 'temperatures', 'in', 'Dubuque,', 'Iowa,']
Skipping line: could not convert string to float: temperatures
['January', '1964', 'through', 'december', '1975,', 'n=144']
Skipping line: could not convert string to float: through
32.475

Based on your original question (before latest edits), I think you can solve your problem in this way.

# from urllib2 import urlopen 
from urllib.request import urlopen #python3

def avg_temp_march(url):
  f = urlopen(url).read()
  data = f.split("\n")[3:] #ingore the first 3 lines
  data = [line.split() for line in data if line!=''] #ignore the empty lines
  data = [map(float, line) for line in data] #Convert all numbers to float
  month_index = 2 # 2 for march
  monthly_sum = sum([line[month_index] for line in data])
  monthly_avg = monthly_sum/len(data)
  return monthly_avg

print avg_temp_march("http://robjhyndman.com/tsdldata/data/cryer2.dat")

Answer 2

Using pandas , the code becomes bit shorter:

import calendar
import pandas a spd
df = pd.read_csv('data5.txt', delim_whitespace=True, skiprows=2,
                 names=calendar.month_abbr[1:])

Now for March:

>>> df.Mar.mean()
32.475000000000001

and for all months:

>>> df.mean()
Jan    16.608333
Feb    20.650000
Mar    32.475000
Apr    46.525000
May    58.091667
Jun    67.500000
Jul    71.716667
Aug    69.333333
Sep    61.025000
Oct    50.975000
Nov    36.650000
Dec    23.641667
dtype: float64