我有一个包含浮点数和 NaN 的数组,但是当我调用numpy.nanmean()该数组时,我得到了NaN结果。这怎么可能?
这是数组的示例ages
[22.0 38.0 26.0 35.0 35.0 nan 54.0 2.0 27.0 14.0 4.0 58.0 20.0 39.0 14.0
55.0 2.0 nan 31.0 nan 35.0 34.0 15.0 28.0 8.0 38.0 nan 19.0 nan nan 40.0
nan nan 66.0 28.0 42.0 nan 21.0 18.0 14.0 40.0 27.0 nan 3.0 19.0 nan nan
nan nan 18.0 7.0 21.0 49.0 29.0 65.0 nan 21.0 28.5 5.0 11.0 22.0 38.0 45.0
...
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形状
(891,)
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并np.nanmean(ages)返回nan
据我所知,在 Julia 中,大多数运算符都可以通过在其前面加上.. 然而我不明白为什么其中一些值得两种方式:
julia> a = rand(1_000_000);
julia> @time a*2;
0.051112 seconds (183.93 k allocations: 17.849 MiB, 7.14% gc time, 89.16% compilation time)
julia> @time a*2;
0.002070 seconds (2 allocations: 7.629 MiB)
julia> @time a.*2;
0.026533 seconds (8.87 k allocations: 8.127 MiB, 93.23% compilation time)
julia> @time a.*2;
0.001575 seconds (4 allocations: 7.630 MiB)
julia> a + 0.1;
ERROR: MethodError: no method matching +(::Vector{Float64}, ::Float64)
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为什么数组广播对 有效*但无效+?
*是什么导致了和之间的性能/分配差异.*?
vector operator-overloading vectorization julia array-broadcasting
我仍在努力理解朱莉娅的类型。例如,假设我想定义一个Animal对象的结构。以打字稿为例,我会这样做:
interface Animal {
sound: string;
}
function sing(animal: Animal) {
console.log(animal.sound);
}
const dog: Animal = {
sound: 'woof',
};
// OR
class Dog implements Animal {
sound = 'woof';
// other Dog methods here
run() {}
}
// Wrong implementations are caught immediately
const cow: Animal = {
noise: 'moo'
}
// Error: Type '{ noise: string; }' is not assignable to type 'Animal'.
// Object literal may only specify known properties, and 'noise' does not …Run Code Online (Sandbox Code Playgroud) 我想知道是否有人可以解释以下之间的区别:
ws = websocket.create_connection('wss://echo.websocket.org')
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和
ws = websocket.WebSocketApp('wss://echo.websocket.org')
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在 Python 包中websocket,因为文档不是很清楚。
我的.csv看起来像这样:
date time
0 20190101 181555700
1 20190101 181545515
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其中格式为YYYYMMDDfordate和HHMMSSMMMfor time(最后一个 MMM 是毫秒)。例如第一行是2019-01-01 18:15:55.700
有没有办法直接解析它而pd.read_csv()不必稍后转换它?仅使用parse_dates不起作用,因为它无法识别格式。我想要的是在我的数据框中有一列,时间戳正确解析为
timestamp
0 2019-01-01 18:15:55.700
Run Code Online (Sandbox Code Playgroud) 我有以下嵌套函数
from typing import Optional
def outer(
outer_foo:int,
outer_bar:Optional[int] = 5
):
return inner(outer_foo, outer_bar)
def inner(
inner_foo:int,
inner_bar:int
):
return inner_foo+inner_bar
print(outer((1)))
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并mypy抛出以下错误:
error: Argument 2 to "inner" has incompatible type "Optional[int]"; expected "int"
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鉴于我给出了int默认值outer_bar,我没有看到潜在的问题。但是,我能够解决 mypy 错误,将代码更改为:
from typing import Optional
def outer(
outer_foo:int,
outer_bar:Optional[int] = None
):
if outer_bar is None:
outer_bar = 5
return inner(outer_foo, outer_bar)
def inner(
inner_foo:int,
inner_bar:int
):
return inner_foo+inner_bar
print(outer((1)))
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这似乎破坏了声明中默认参数的用处。这是最好的/Python式的方法吗?