通过python迭代在numpy/scipy中建立一个数组? [英] Building up an array in numpy/scipy by iteration in Python?

问题描述

通常，我通过迭代一些数据来构建数组，例如:

Often, I am building an array by iterating through some data, e.g.:

my_array = []
for n in range(1000):
  # do operation, get value 
  my_array.append(value)
# cast to array
my_array = array(my_array)

我发现我必须首先构建一个列表，然后将其(使用数组")转换为数组.有没有办法解决这些问题?所有这些强制转换调用使代码变得混乱...如何从头开始以数组形式迭代构建"my_array"?

I find that I have to first build a list and then cast it (using "array") to an array. Is there a way around these? All these casting calls clutter the code... how can I iteratively build up "my_array", with it being an array from the start?

推荐答案

如果我正确理解了您的问题，这应该可以满足您的要求:

If i understand your question correctly, this should do what you want:

# the array passed into your function
ax = NP.random.randint(10, 99, 20).reshape(5, 4)

# just define a function to operate on some data
fnx = lambda x : NP.sum(x)**2

# apply the function directly to the numpy array
new_row = NP.apply_along_axis(func1d=fnx, axis=0, arr=ax)

# 'append' the new values to the original array
new_row = new_row.reshape(1,4)
ax = NP.vstack((ax, new_row))

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