random.random((2,3)) >>> a *= 3 >>> a array([[3, 3, 3], [3, 3, 3]]) >>> b += a >>> b array([[ 3.417022 , 3.72032449, 3.00011437], [ 3.30233257, 3.14675589, 3.09233859]]) >>> a += b # b is not automatically conv
27, 64]) >>> # equivalent to a[0:6:2] = 1000; >>> # from start to position 6, exclusive, set every 2nd element to 1000 >>> a[:6:2] = 1000 >>> a array([1000, 1, 1000, 27, 1000, 125, 216, 343, 512,
(2,3)) >>> a *= 3 >>> a array([[3, 3, 3], [3, 3, 3]]) >>> b += a >>> b array([[ 3.417022 , 3.72032449, 3.00011437], [ 3.30233257, 3.14675589, 3.09233859]]) >>> a += b # b is not automatically converted to integer type Traceback (most recent call last): ....
Regarding the difference in output between your two expressions: in the first, you are creating an array of dtypeobject, where each entry is a Python integer, and Python integers have arbitrary precision. In the second, you are creating an array of dtypeint64, where each entry is a 64-bit...
(2, 3)) >>> a *= 3 >>> a array([[3, 3, 3], [3, 3, 3]]) >>> b += a >>> b array([[3.51182162, 3.9504637 , 3.14415961], [3.94864945, 3.31183145, 3.42332645]]) >>> a += b # b is not automatically converted to integer type Traceback (most recent call last): .....
(2,3)) >>> a *= 3 >>> a array([[3, 3, 3], [3, 3, 3]]) >>> b += a >>> b array([[ 3.417022 , 3.72032449, 3.00011437], [ 3.30233257, 3.14675589, 3.09233859]]) >>> a += b # b is not automatically converted to integer type Traceback (most recent call last): ....
array.fill(scalar)的行为可能略有不同](release/1.24.0-notes.html#array-fill-scalar-may-behave-slightly-different) 子数组到对象的转换现在会进行拷贝](release/1.24.0-notes.html#subarray-to-object-cast-now-copies) 返回的数组将尊重 dtype 参数对象的唯一性](release/1.24.0-notes.html#returned-arrays...
empty_like、full_like、ones_like和zeros_like现在接受一个shape关键字参数,可用于创建一个新的数组作为原型,同时覆盖其形状。在与__array_function__协议结合使用时非常有用,允许从类似 NumPy 的库创建新的任意形状数组,当这样的数组用作原型时。浮点标量实现as_integer_ratio以匹配内置的浮点数...
(2, 3)) >>> a *= 3 >>> a array([[3, 3, 3], [3, 3, 3]]) >>> b += a >>> b array([[3.51182162, 3.9504637 , 3.14415961], [3.94864945, 3.31183145, 3.42332645]]) >>> a += b # b is not automatically converted to integer type Traceback (most recent call last): .....
The NumPy nddary: A Multidimensional Array Object One of the key features of NumPy is its N-demensional array object(N维数数组对象), or ndarray, which is a fast, flexible container(容器) for large datasets in Python. Arrays enable you to perform(执行) mathematical operations on whole blocks...