Question

所以这是我的文本文件，我想计算每年的平均值：

1969    324.000
1970    330.190
1970    326.720
1970    327.130
1971    326.970
1971    331.200
1971    329.430
1971    335.770
1971    337.600

这是我从另一个问题得到的代码，但它仍然存在错误：

In[2]: result = {}
with open('filename.txt', 'r') as f:
   for line in f:
       year, num = line.split()
       year = int(year)
       num = float(num)
       try:
           result[year].append(num)
       except KeyError:
           result[year] = [num]

In[3]: for k, v in sorted(result.items()):
   print('Year: {}\tAverage: {:.2f}'.format(k, sum(v) / len(v)))

我的错误：

Traceback (most recent call last):
  File "C:\Users\fra02.sek\Desktop\average.py", line 4, in <module>
year, num = line.split()
ValueError: too many values to unpack (expected 2)

Answer 1

修正了缩进并添加了空行检查：

for fname in glob.glob(path):
    fname = fname.replace(r'\2016', '/2016')
    f = pd.DataFrame(pd.read_csv(fname))
    f = f.replace({'Hour': {'24:00:00': '00:00'}})
    f['Date'] = pd.to_datetime(f['Date']).dt.strftime('%d/%m/%Y')
    f['Hour'] = pd.to_datetime(f['Hour']).dt.strftime('%H:%M')

    m = f['Hour'] == '00:00'
    dates = (pd.to_datetime(f['Date']) + pd.Timedelta(1, unit='d')).dt.strftime('%d/%m/%Y')
    f['Date'] = np.where(m, dates, f['Date'])

    print(fname)
    if a == 0:
        f_2016['Date'] = f['Date']
        f_2016['Hour'] = f['Hour']
        a = 1
    f_2016 = pd.merge(f_2016, f, on=['Date', 'Hour'])
    print(pd.DataFrame.head(f_2016, n=100))

另一种解决方案，我认为更具可读性：

result = {}
with open('filename.txt', 'r') as f:
    for line in f:
        if line.strip()!='':
            year, num = line.split()
            year = int(year)
            num = float(num)
            try:
                result[year].append(num)
            except KeyError:
                result[year] = [num]

for k, v in sorted(result.items()):
    print('Year: {}\tAverage: {:.2f}'.format(k, sum(v) / len(v)))

如何计算python中每年文本文件的平均值？

1 个答案: