[英]Python check when each column reaches a number and save that column number
I have an array filled with arrays using numpy split like the following我有一个数组填充了 arrays 使用 numpy 拆分如下
[array([3, 0, 0]),
array([1, 1, 1]),
array([2 , 2, 4]),
array([0, 1, 2]),
array([0, 1, 2]),
array([0, 2, 2])]
I want to check when each column reaches the number 3 or higher and then save that column number as another array or list.我想检查每列何时达到数字 3 或更高,然后将该列号保存为另一个数组或列表。 I also want it to write out the last column number if it never exceeded 3. So the answer to this example should be
如果它从未超过 3,我还希望它写出最后一列编号。所以这个例子的答案应该是
[1, 6, 3]
Which means first column reached the number 3 on row 1, column 2 never reached the number 3 and column 3 reached the number 3 on row 3.这意味着第一列达到了第 1 行的数字 3,第 2 列从未达到数字 3,第 3 列达到了第 3 行的数字 3。
I have tried several things but none of them has worked.我尝试了几件事,但都没有奏效。
You can use argmax
from numpy
to achieve this:您可以使用
argmax
中的numpy
来实现此目的:
import numpy as np
split_data = [
np.array([3, 0, 0]),
np.array([1, 1, 1]),
np.array([2, 2, 4]),
np.array([0, 1, 2]),
np.array([0, 1, 2]),
np.array([0, 2, 2]),
]
print(f"split_data[0].shape {split_data[0].shape}")
print(f"split_data\n{split_data}")
# convert the list of arrays to a single matrix
data = np.stack(split_data)
print(f"data.shape {data.shape}")
print(f"data\n{data}")
# create a boolean array where the data is over 3
over3 = data >= 3
print(f"over3\n{over3}")
# this finds out the row where 3 was reached
index = np.argmax(over3, axis=0)
print(f"index {index}")
# you start counting from one, so increase that
index = index + 1
print(f"index increased {index}")
# this finds out if a column never reached 3
never = np.sum(over3, axis=0)
print(f"never {never}")
# if the sum is 0, then it never went over
# use that to update the index found
index[never == 0] = data.shape[0]
print(f"index {index}")
Which will produce哪个会产生
split_data[0].shape (3,)
split_data
[array([3, 0, 0]), array([1, 1, 1]), array([2, 2, 4]), array([0, 1, 2]), array([0, 1, 2]), array([0, 2, 2])]
data.shape (6, 3)
data
[[3 0 0]
[1 1 1]
[2 2 4]
[0 1 2]
[0 1 2]
[0 2 2]]
over3
[[ True False False]
[False False False]
[False False True]
[False False False]
[False False False]
[False False False]]
index [0 0 2]
index increased [1 1 3]
never [1 0 1]
index [1 6 3]
Cheers!干杯!
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