Python program to demonstrate the example how does numpy.where() work # Import numpyimportnumpyasnp# Creating an arrayarr=np.arange(100)# Display original arrayprint("Original Array:\n",arr,"\n")# Using numpy whereres=np.where(arr>50)# Display resultprint("Result:\n",res,"\n") ...
The numpy documentation discourages the use of np.where with just passing a condition and recommends np.asarray(condition).nonzero() instead. For cleanliness of code, should we adopt this recommendation, at least in the examples? Or are there good reasons why we do that? StefanieSenger added...
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np.where 是NumPy 库中的一个函数,它允许你根据条件数组来选择数据。如果你想要在 np.where 中创建迭代条件,你可以使用循环结构来构建条件数组,然后将其传递给 np.where 函数。 基础概念 np.where 函数的基本语法如下: 代码语言:txt 复制 np.where(condition[, x, y]) condition 是一个布尔数组,用于决定从 ...
Now, we’re going to use np.where to find the values greater than 2. To do this, we’ll callnp.where(). Inside of the function, we’ll have a condition that will test if the elements are greater than 2. Then we’ll output “True” if the value is greater than 2, and “False...
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result = np.where(condition) print(result) Output: (array([2, 3], dtype=int64),) In this example, we have defined a condition array > 25. Thenumpy.wherefunction checks this condition for each element in the array and returns a tuple containing the indices of the elements that meet the...
The numpy.where() do have 2 'operational modes', first one returns the indices, where condition is True and if optional parameters x and y are present (same shape as condition, or broadcastable to such shape!), it will return values from x when condition is True otherwise from y. So,...