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What does template mean?
...Factorial<N - 1>::value };
};
template <>
struct Factorial<0>
{
enum { value = 1 };
};
// Factorial<4>::value == 24
// Factorial<0>::value == 1
void foo()
{
int x = Factorial<4>::value; // == 24
int y = Factorial<0>::value; // == 1
}
...
Fixed point vs Floating point number
...d represent would be 99999.99999 and the smallest non-zero number would be 00000.00001. Every bit of code that processes such numbers has to have built-in knowledge of where the decimal point is.
A floating point number does not reserve a specific number of bits for the integer part or the fraction...
How do you represent a JSON array of strings?
...
303
I'll elaborate a bit more on ChrisR awesome answer and bring images from his awesome reference....
Get __name__ of calling function's module in Python
...ef info(msg):
frm = inspect.stack()[1]
mod = inspect.getmodule(frm[0])
print '[%s] %s' % (mod.__name__, msg)
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Replace only text inside a div using jquery
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edited Oct 29 '14 at 20:50
cuSK
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answered Aug 8 '12 at 14:56
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Difference between `mod` and `rem` in Haskell
...
20
I had the same question about rem and mod in Clojure, and this was the answer.
– noahlz
Jul 11 '12 at...
What is the most efficient way to create a dictionary of two pandas Dataframe columns?
...Speed comparion (using Wouter's method)
In [6]: df = pd.DataFrame(randint(0,10,10000).reshape(5000,2),columns=list('AB'))
In [7]: %timeit dict(zip(df.A,df.B))
1000 loops, best of 3: 1.27 ms per loop
In [8]: %timeit pd.Series(df.A.values,index=df.B).to_dict()
1000 loops, best of 3: 987 us per loop...
MediaHelper 媒体助手扩展:从媒体文件提取元数据和专辑封面 · App Inventor 2 中文网
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版本
日期
修改内容
1.0
2021-07-06
初始版本
1.1
2024-09-04
Android 14 适配
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应用截图
文件列表示例
...
Check whether a string matches a regex in JS
... regex.test() if all you want is a boolean result:
console.log(/^([a-z0-9]{5,})$/.test('abc1')); // false
console.log(/^([a-z0-9]{5,})$/.test('abc12')); // true
console.log(/^([a-z0-9]{5,})$/.test('abc123')); // true
...and you could remove the () from your regexp since you've no n...
Pandas aggregate count distinct
...
How about either of:
>>> df
date duration user_id
0 2013-04-01 30 0001
1 2013-04-01 15 0001
2 2013-04-01 20 0002
3 2013-04-02 15 0002
4 2013-04-02 30 0002
>>> df.groupby("date").agg({"duration": np.sum, "user_id...
