Python：尝试创建两个函数。一个遍历二维列表的列，而另一个遍历行_Python_Arrays_Python 3.x_For Loop_Multidimensional Array

Python：尝试创建两个函数。一个遍历二维列表的列，而另一个遍历行

python arrays python-3.x for-loop

Python：尝试创建两个函数。一个遍历二维列表的列，而另一个遍历行,python,arrays,python-3.x,for-loop,multidimensional-array,Python,Arrays,Python 3.x,For Loop,Multidimensional Array,我引用main（）来测试它。最后，它告诉我TypeError：列表索引必须是整数或片，而不是NoneType 那么，什么是合适的占位符，而不是None？还有，我做的for循环对吗请让我知道。这里的新手（这是很明显的）。您确实使用了无作为索引。相反，对于average_hour，您应该迭代一天中的所有小时（而不是无小时）。与平均日类似： def average_hour (temps, hour): i = 0 for i in temps[None][i]:

我引用

main（）

来测试它。最后，它告诉我

TypeError：列表索引必须是整数或片，而不是NoneType

那么，什么是合适的占位符，而不是

None

？还有，我做的for循环对吗

请让我知道。这里的新手（这是很明显的）。

您确实使用了

无

作为索引。相反，对于

average_hour

，您应该迭代一天中的所有小时（而不是无小时）。与

平均日类似

：

def average_hour (temps, hour):
      i = 0
      for i in temps[None][i]:
            total = total + temps[None][i]
      average = total/(i+1)
      return average

def average_day (temps, day):
      i = 0
      for i in temps[i][None]:
            total = total + temps[i][None]
      average = total/(i+1)
      return average      

def main():
      temps = [
      [73.0, 73.9, 73.0, 73.9, 73.9, 73.9, 73.0, 71.1, 71.1, 71.1, 70.0, 70.0, 70.0, 70.0, 70.0, 71.1, 71.1, 71.6, 71.1, 71.1, 70.0, 71.1, 70.0, 71.1],
      [63.0, 63.0, 62.1, 62.1, 62.1, 62.1, 62.1, 62.1, 62.1, 62.1, 62.1, 62.1, 62.1, 62.1, 61.0, 62.6, 62.6, 62.1, 62.1, 62.1, 62.1, 62.1, 62.1, 62.1],
      [68.0, 68.0, 69.1, 69.1, 69.1, 73.9, 75.0, 75.9, 81.0, 81.0, 82.0, 84.0, 80.1, 78.1, 75.9, 77.0, 80.1, 81.0, 82.0, 81.0, 81.0, 79.0, 78.1, 78.1],
      [78.1, 78.1, 78.1, 77.0, 77.0, 77.0, 75.9, 75.0, 75.2, 75.9, 73.9, 73.9, 73.9, 73.0, 73.0, 73.0, 73.9, 73.9, 73.0, 73.4, 73.0, 73.0, 75.0, 75.0],
      [71.1, 71.1, 70.0, 70.0, 70.0, 70.0, 70.0, 70.0, 70.0, 70.0, 69.1, 69.1, 69.1, 69.1, 68.0, 66.9, 66.0, 66.0, 66.0, 66.0, 66.0, 66.0, 64.9, 64.9],
      [61.0, 61.0, 61.0, 61.0, 61.0, 61.0, 60.1, 60.1, 60.1, 60.1, 60.1, 60.1, 60.1, 60.1, 60.1, 60.1, 60.1, 60.1, 60.1, 61.0, 61.0, 61.0, 64.9, 64.9],
      [57.0, 55.0, 55.0, 55.0, 54.0, 54.0, 55.0, 54.0, 55.0, 55.9, 55.9, 55.0, 55.0, 57.0, 62.1, 66.9, 71.1, 73.0, 75.0, 77.0, 78.1, 78.1, 78.1, 75.9],
      [62.1, 61.0, 61.0, 60.1, 60.1, 59.0, 59.0, 61.0, 66.9, 72.0, 75.9, 78.1, 78.1, 78.1, 78.1, 78.1, 77.0, 75.0, 72.0, 71.1, 70.0, 68.0, 66.0, 64.0],
      [63.0, 62.1, 61.0, 61.0, 59.0, 60.1, 59.0, 60.1, 66.9, 73.9, 75.9, 78.1, 81.0, 79.0, 81.0, 79.0, 79.0, 75.9, 73.0, 72.0, 71.1, 71.1, 71.1, 70.0],
      [70.0, 69.1, 70.0, 69.1, 69.1, 69.1, 69.1, 69.1, 68.0, 68.0, 68.0, 70.0, 69.8, 70.0, 70.0, 70.0, 71.1, 70.0, 70.0, 71.1, 71.1, 71.1, 73.0, 73.0],
      [61.0, 61.0, 60.1, 59.0, 59.0, 59.0, 57.9, 59.0, 59.0, 60.1, 61.0, 61.0, 63.0, 66.9, 66.0, 71.1, 71.1, 73.9, 72.0, 75.9, 75.0, 75.9, 75.9, 75.0],
      [62.1, 62.1, 62.1, 61.0, 62.1, 61.0, 62.1, 62.1, 63.0, 62.1, 61.0, 61.0, 61.0, 61.0, 64.0, 64.9, 66.0, 66.0, 68.0, 70.0, 66.0, 64.0, 62.1, 62.1],
      [63.0, 63.0, 64.0, 64.0, 64.9, 63.0, 63.0, 64.0, 68.0, 72.0, 75.0, 77.0, 79.0, 79.0, 79.0, 79.0, 78.1, 75.9, 73.0, 72.0, 71.1, 71.1, 70.0, 69.1],
      [69.1, 68.0, 66.0, 66.0, 64.9, 64.0, 64.0, 63.0, 63.0, 64.0, 64.0, 64.0, 64.9, 68.0, 73.0, 75.0, 77.0, 79.0, 80.1, 80.1, 79.0, 77.0, 71.1, 64.0],
      [57.9, 57.9, 59.0, 59.0, 57.0, 55.9, 55.0, 55.0, 60.1, 64.9, 70.0, 73.0, 73.0, 73.9, 75.0, 73.9, 75.0, 72.0, 61.0, 57.0, 55.9, 54.0, 53.1, 53.1],
      [53.1, 53.1, 52.0, 52.0, 51.1, 51.1, 54.0, 55.0, 64.0, 69.1, 73.0, 77.0, 79.0, 81.0, 81.0, 81.0, 78.1, 75.0, 73.0, 72.0, 68.0, 66.9, 66.0, 66.0],
      [62.1, 61.0, 57.9, 57.0, 57.0, 55.0, 54.0, 54.0, 57.0, 63.0, 66.0, 66.9, 70.0, 72.0, 72.0, 72.0, 71.1, 68.0, 63.0, 57.9, 59.0, 55.0, 55.0, 53.1],
      [53.1, 51.1, 48.9, 48.0, 46.9, 45.0, 44.1, 45.0, 48.0, 51.1, 54.0, 55.0, 55.9, 57.9, 59.0, 60.1, 59.0, 57.0, 52.0, 46.9, 46.0, 43.0, 45.0, 42.1],
      [42.1, 39.9, 39.9, 39.9, 39.9, 39.0, 37.9, 39.9, 45.0, 50.0, 55.0, 57.0, 59.0, 60.1, 61.0, 61.0, 61.0, 57.9, 52.0, 48.0, 46.9, 45.0, 45.0, 45.0],
      [45.0, 43.0, 43.0, 43.0, 42.1, 42.1, 42.1, 43.0, 52.0, 57.0, 61.0, 64.0, 68.0, 70.0, 71.1, 70.0, 69.1, 66.9, 62.1, 57.0, 54.0, 54.0, 53.1, 52.0],
      [51.1, 50.0, 50.0, 51.1, 51.1, 51.1, 50.0, 52.0, 57.9, 64.0, 70.0, 73.0, 75.0, 75.0, 75.9, 75.0, 73.0, 70.0, 66.9, 63.0, 62.1, 60.1, 60.8, 61.0],
      [55.9, 57.0, 57.0, 57.0, 57.0, 57.0, 55.9, 55.0, 55.0, 55.0, 55.0, 55.0, 55.0, 54.0, 54.0, 54.0, 54.0, 54.0, 54.0, 54.0, 54.0, 54.0, 53.1, 53.1],
      [57.0, 55.9, 55.0, 54.0, 54.0, 53.1, 53.1, 54.0, 54.0, 53.1, 54.0, 54.0, 54.0, 54.0, 54.0, 54.0, 54.0, 53.1, 53.1, 53.1, 62.1, 68.0, 73.0, 77.0],
      [63.0, 59.0, 57.9, 57.9, 55.9, 55.9, 55.0, 55.9, 60.1, 63.0, 66.0, 69.1, 72.0, 72.0, 73.9, 73.0, 70.0, 68.0, 64.0, 61.0, 59.0, 61.0, 57.9, 57.9],
      [55.9, 55.9, 55.0, 55.0, 54.0, 55.0, 55.0, 55.0, 55.0, 55.0, 55.0, 55.9, 57.0, 57.0, 57.9, 61.0, 64.0, 68.0, 73.0, 75.9, 78.1, 78.1, 77.0, 78.1],
      [64.9, 66.0, 64.9, 64.0, 64.0, 63.0, 61.0, 61.0, 61.0, 61.0, 63.0, 66.9, 64.0, 66.0, 64.0, 64.0, 64.0, 62.1, 61.0, 60.1, 59.0, 57.9, 57.9, 57.9],
      [57.9, 57.9, 57.9, 57.9, 57.9, 57.9, 57.0, 57.9, 59.0, 60.1, 59.0, 60.1, 60.1, 61.0, 63.0, 64.9, 66.0, 66.0, 66.0, 66.0, 66.9, 66.9, 66.0, 66.0],
      [70.0, 70.0, 71.1, 71.1, 71.1, 71.1, 70.0, 70.0, 69.1, 71.1, 71.1, 73.0, 73.9, 75.9, 77.0, 73.9, 73.0, 72.0, 72.0, 72.0, 72.0, 72.0, 72.0, 72.0],
      [72.0, 71.1, 71.1, 71.1, 70.0, 68.0, 68.0, 68.0, 66.2, 66.9, 66.0, 66.0, 69.1, 70.0, 70.0, 73.0, 73.9, 75.9, 77.0, 79.0, 78.1, 75.0, 72.0, 69.1],
      [66.9, 66.0, 66.0, 66.0, 66.0, 64.9, 64.0, 63.0, 64.0, 66.0, 69.1, 71.1, 73.0, 73.0, 73.0, 73.9, 73.0, 70.0, 61.0, 59.0, 57.0, 55.9, 54.0, 53.1],
      [51.1, 51.1, 51.1, 51.1, 51.1, 52.0, 51.1, 51.1, 54.0, 60.1, 64.0, 66.0, 68.0, 69.1, 68.0, 68.0, 66.9, 64.9, 63.0, 60.1, 59.0, 60.1, 57.9, 57.0]
      ]

      print("Average Hour:", average_hour(temps, 0)) # Should be ~61.3
      print("Average Day:", average_day(temps, 0))  # Should be ~71.5

正如评论中提到的，有一些方法可以缩短这一时间。其中一种更具可读性的方法是使用（或@PadraicCunningham指出的底层），例如：

您确实在使用

None

作为索引。相反，对于

average_hour

，您应该迭代一天中的所有小时（而不是无小时）。与

平均日类似

：

def average_hour (temps, hour):
      i = 0
      for i in temps[None][i]:
            total = total + temps[None][i]
      average = total/(i+1)
      return average

def average_day (temps, day):
      i = 0
      for i in temps[i][None]:
            total = total + temps[i][None]
      average = total/(i+1)
      return average      

def main():
      temps = [
      [73.0, 73.9, 73.0, 73.9, 73.9, 73.9, 73.0, 71.1, 71.1, 71.1, 70.0, 70.0, 70.0, 70.0, 70.0, 71.1, 71.1, 71.6, 71.1, 71.1, 70.0, 71.1, 70.0, 71.1],
      [63.0, 63.0, 62.1, 62.1, 62.1, 62.1, 62.1, 62.1, 62.1, 62.1, 62.1, 62.1, 62.1, 62.1, 61.0, 62.6, 62.6, 62.1, 62.1, 62.1, 62.1, 62.1, 62.1, 62.1],
      [68.0, 68.0, 69.1, 69.1, 69.1, 73.9, 75.0, 75.9, 81.0, 81.0, 82.0, 84.0, 80.1, 78.1, 75.9, 77.0, 80.1, 81.0, 82.0, 81.0, 81.0, 79.0, 78.1, 78.1],
      [78.1, 78.1, 78.1, 77.0, 77.0, 77.0, 75.9, 75.0, 75.2, 75.9, 73.9, 73.9, 73.9, 73.0, 73.0, 73.0, 73.9, 73.9, 73.0, 73.4, 73.0, 73.0, 75.0, 75.0],
      [71.1, 71.1, 70.0, 70.0, 70.0, 70.0, 70.0, 70.0, 70.0, 70.0, 69.1, 69.1, 69.1, 69.1, 68.0, 66.9, 66.0, 66.0, 66.0, 66.0, 66.0, 66.0, 64.9, 64.9],
      [61.0, 61.0, 61.0, 61.0, 61.0, 61.0, 60.1, 60.1, 60.1, 60.1, 60.1, 60.1, 60.1, 60.1, 60.1, 60.1, 60.1, 60.1, 60.1, 61.0, 61.0, 61.0, 64.9, 64.9],
      [57.0, 55.0, 55.0, 55.0, 54.0, 54.0, 55.0, 54.0, 55.0, 55.9, 55.9, 55.0, 55.0, 57.0, 62.1, 66.9, 71.1, 73.0, 75.0, 77.0, 78.1, 78.1, 78.1, 75.9],
      [62.1, 61.0, 61.0, 60.1, 60.1, 59.0, 59.0, 61.0, 66.9, 72.0, 75.9, 78.1, 78.1, 78.1, 78.1, 78.1, 77.0, 75.0, 72.0, 71.1, 70.0, 68.0, 66.0, 64.0],
      [63.0, 62.1, 61.0, 61.0, 59.0, 60.1, 59.0, 60.1, 66.9, 73.9, 75.9, 78.1, 81.0, 79.0, 81.0, 79.0, 79.0, 75.9, 73.0, 72.0, 71.1, 71.1, 71.1, 70.0],
      [70.0, 69.1, 70.0, 69.1, 69.1, 69.1, 69.1, 69.1, 68.0, 68.0, 68.0, 70.0, 69.8, 70.0, 70.0, 70.0, 71.1, 70.0, 70.0, 71.1, 71.1, 71.1, 73.0, 73.0],
      [61.0, 61.0, 60.1, 59.0, 59.0, 59.0, 57.9, 59.0, 59.0, 60.1, 61.0, 61.0, 63.0, 66.9, 66.0, 71.1, 71.1, 73.9, 72.0, 75.9, 75.0, 75.9, 75.9, 75.0],
      [62.1, 62.1, 62.1, 61.0, 62.1, 61.0, 62.1, 62.1, 63.0, 62.1, 61.0, 61.0, 61.0, 61.0, 64.0, 64.9, 66.0, 66.0, 68.0, 70.0, 66.0, 64.0, 62.1, 62.1],
      [63.0, 63.0, 64.0, 64.0, 64.9, 63.0, 63.0, 64.0, 68.0, 72.0, 75.0, 77.0, 79.0, 79.0, 79.0, 79.0, 78.1, 75.9, 73.0, 72.0, 71.1, 71.1, 70.0, 69.1],
      [69.1, 68.0, 66.0, 66.0, 64.9, 64.0, 64.0, 63.0, 63.0, 64.0, 64.0, 64.0, 64.9, 68.0, 73.0, 75.0, 77.0, 79.0, 80.1, 80.1, 79.0, 77.0, 71.1, 64.0],
      [57.9, 57.9, 59.0, 59.0, 57.0, 55.9, 55.0, 55.0, 60.1, 64.9, 70.0, 73.0, 73.0, 73.9, 75.0, 73.9, 75.0, 72.0, 61.0, 57.0, 55.9, 54.0, 53.1, 53.1],
      [53.1, 53.1, 52.0, 52.0, 51.1, 51.1, 54.0, 55.0, 64.0, 69.1, 73.0, 77.0, 79.0, 81.0, 81.0, 81.0, 78.1, 75.0, 73.0, 72.0, 68.0, 66.9, 66.0, 66.0],
      [62.1, 61.0, 57.9, 57.0, 57.0, 55.0, 54.0, 54.0, 57.0, 63.0, 66.0, 66.9, 70.0, 72.0, 72.0, 72.0, 71.1, 68.0, 63.0, 57.9, 59.0, 55.0, 55.0, 53.1],
      [53.1, 51.1, 48.9, 48.0, 46.9, 45.0, 44.1, 45.0, 48.0, 51.1, 54.0, 55.0, 55.9, 57.9, 59.0, 60.1, 59.0, 57.0, 52.0, 46.9, 46.0, 43.0, 45.0, 42.1],
      [42.1, 39.9, 39.9, 39.9, 39.9, 39.0, 37.9, 39.9, 45.0, 50.0, 55.0, 57.0, 59.0, 60.1, 61.0, 61.0, 61.0, 57.9, 52.0, 48.0, 46.9, 45.0, 45.0, 45.0],
      [45.0, 43.0, 43.0, 43.0, 42.1, 42.1, 42.1, 43.0, 52.0, 57.0, 61.0, 64.0, 68.0, 70.0, 71.1, 70.0, 69.1, 66.9, 62.1, 57.0, 54.0, 54.0, 53.1, 52.0],
      [51.1, 50.0, 50.0, 51.1, 51.1, 51.1, 50.0, 52.0, 57.9, 64.0, 70.0, 73.0, 75.0, 75.0, 75.9, 75.0, 73.0, 70.0, 66.9, 63.0, 62.1, 60.1, 60.8, 61.0],
      [55.9, 57.0, 57.0, 57.0, 57.0, 57.0, 55.9, 55.0, 55.0, 55.0, 55.0, 55.0, 55.0, 54.0, 54.0, 54.0, 54.0, 54.0, 54.0, 54.0, 54.0, 54.0, 53.1, 53.1],
      [57.0, 55.9, 55.0, 54.0, 54.0, 53.1, 53.1, 54.0, 54.0, 53.1, 54.0, 54.0, 54.0, 54.0, 54.0, 54.0, 54.0, 53.1, 53.1, 53.1, 62.1, 68.0, 73.0, 77.0],
      [63.0, 59.0, 57.9, 57.9, 55.9, 55.9, 55.0, 55.9, 60.1, 63.0, 66.0, 69.1, 72.0, 72.0, 73.9, 73.0, 70.0, 68.0, 64.0, 61.0, 59.0, 61.0, 57.9, 57.9],
      [55.9, 55.9, 55.0, 55.0, 54.0, 55.0, 55.0, 55.0, 55.0, 55.0, 55.0, 55.9, 57.0, 57.0, 57.9, 61.0, 64.0, 68.0, 73.0, 75.9, 78.1, 78.1, 77.0, 78.1],
      [64.9, 66.0, 64.9, 64.0, 64.0, 63.0, 61.0, 61.0, 61.0, 61.0, 63.0, 66.9, 64.0, 66.0, 64.0, 64.0, 64.0, 62.1, 61.0, 60.1, 59.0, 57.9, 57.9, 57.9],
      [57.9, 57.9, 57.9, 57.9, 57.9, 57.9, 57.0, 57.9, 59.0, 60.1, 59.0, 60.1, 60.1, 61.0, 63.0, 64.9, 66.0, 66.0, 66.0, 66.0, 66.9, 66.9, 66.0, 66.0],
      [70.0, 70.0, 71.1, 71.1, 71.1, 71.1, 70.0, 70.0, 69.1, 71.1, 71.1, 73.0, 73.9, 75.9, 77.0, 73.9, 73.0, 72.0, 72.0, 72.0, 72.0, 72.0, 72.0, 72.0],
      [72.0, 71.1, 71.1, 71.1, 70.0, 68.0, 68.0, 68.0, 66.2, 66.9, 66.0, 66.0, 69.1, 70.0, 70.0, 73.0, 73.9, 75.9, 77.0, 79.0, 78.1, 75.0, 72.0, 69.1],
      [66.9, 66.0, 66.0, 66.0, 66.0, 64.9, 64.0, 63.0, 64.0, 66.0, 69.1, 71.1, 73.0, 73.0, 73.0, 73.9, 73.0, 70.0, 61.0, 59.0, 57.0, 55.9, 54.0, 53.1],
      [51.1, 51.1, 51.1, 51.1, 51.1, 52.0, 51.1, 51.1, 54.0, 60.1, 64.0, 66.0, 68.0, 69.1, 68.0, 68.0, 66.9, 64.9, 63.0, 60.1, 59.0, 60.1, 57.9, 57.0]
      ]

      print("Average Hour:", average_hour(temps, 0)) # Should be ~61.3
      print("Average Day:", average_day(temps, 0))  # Should be ~71.5

正如评论中提到的，有一些方法可以缩短这一时间。其中一种更具可读性的方法是使用（或@PadraicCunningham指出的底层），例如：

temps[i][None]

应该做什么？“无”怎么可能是一个索引？@PadraicCunningham是的，但是“无”的占位符应该是什么呢？您也没有声明总计、使用小时或天，小时和天如何符合您的逻辑？天和小时参数的作用是什么？好的，我可以在

average\u hour

和

average\u day

的开头写

total=0

吗？

temps[I][None]

average\u hour

和

average\u day

的开头写

total=0

吗？好的。这很有效。现在回答我的问题，让我正确理解！那么，为什么在

average_hour

函数中使用

for day

，在

average_day

函数中使用

for hour

？另外，

+=

做什么？

sum（row[day]表示temps中的row）/len（temps）

表示things中的things

是迭代iterable元素（例如

列表

）的pythonical方法

x+=y

是通往

x=x+y

@schwobaseggl的捷径是的，我知道我们正在迭代。但为什么我们使用的基本上是相反的。就像我们在

day

函数中使用的

hour

。还有，好的。

+=

现在有意义了。@padraiccningham考虑到涉及的temp（31）及其行（24）的长度，

sum（temps[day]）/len（temps[day]）

似乎更合适。这很有效。现在回答我的问题，让我正确理解！那么，为什么在

average_hour

函数中使用

for day

，在

average_day

函数中使用

for hour

？另外，

+=

做什么？

sum（row[day]表示temps中的row）/len（temps）

表示things中的things

是迭代iterable元素（例如

列表

）的pythonical方法

x+=y

是通往

x=x+y

@schwobaseggl的捷径是的，我知道我们正在迭代。但为什么我们使用的基本上是相反的。就像我们在

day

函数中使用的

hour

。还有，好的。

+=

现在是有意义的了。@padraiccanningham考虑到涉及的temp（31）及其行（24）的长度，

sum（temps[day]）/len（temps[day]）

def average_hour(temps, hour):
    days = [day[hour] for day in temps]
    return sum(days)/len(days)
    # or even shorter: return sum(day[hour] for day in temps)/len(temps)

def average_day(temps, day):
    return sum(temps[day])/len(temps[day])