Python def is_divisible(a, b): return b != 0 and a % b == 0 In this function, if b is 0, then a / b isn’t defined. So, the numbers aren’t divisible. If b is different from 0, then the result will depend on the remainder of the division....
deffun(x: int) ->str:return[x]>>> fun('asd') ['asd'] 参考文章: https://blog.csdn.net/allway2/article/details/116101988 https://blog.csdn.net/weixin_45798684/article/details/106037680 https://stackoverflow.com/questions/14379753/what-does-mean-in-python-function-definitions https://stack...
ds.rolling(x).sum() #依次计算相邻x个元素的和 ds.rolling(x).mean() #依次计算相邻x个元素的算数平均 ds.rolling(x).val() #依次计算相邻x个元素的方差 ds.rolling(x).std() #依次计算相邻x个元素的方差 ds.rolling(x).min() #依次计算相邻x个元素的最小值 ds.rolling(x).max() #依次计算相邻...
# CI = mu +- t * (standard error for each sample statistic is different) def sample_mean_confidence_interval(data, confidence=0.95): a = 1.0 * np.array(data) n = len(a) m, se = np.mean(a),stats.sem(a) #h = stats.t.ppf((1+confidence)/2.,n-1)*se # two tail h = st...
mean=[0,0]cov=[[1,0],[0,1]]rv=multivariate_normal(mean,cov)sample=rv.rvs(size=1000) 1. 2. 3. 4. 5. 6. 7. 跨技术栈交互的时序图: 数据库Python Service用户数据库Python Service用户提交数据请求查询数据返回数据返回结果 配置详解
# 移动平均图 defdraw_trend(timeSeries,size):f=plt.figure(facecolor='white')# 对size个数据进行移动平均 rol_mean=timeSeries.rolling(window=size).mean()# 对size个数据进行加权移动平均 rol_weighted_mean=pd.ewma(timeSeries,span=size)timeSeries.plot(color='blue',label='Original')rolmean.plot(col...
https://stackoverflow.com/questions/14379753/what-does-mean-in-python-function-definitionshttps://www.python.org/dev/peps/pep-3107/Wow, I missed quite a broad area of knowledge - not only return value annotations, but also parameter annotations. Thank you very much :)...
But most of time, it is used because we don't care its value later. For example, there is a function which returns two values, and we only are interested in the second return value. At the mean time, we don't want to create a variable because naming a variable is boring and may ...
torch.mean() # A (B x L x T) indices = torch.max(A, dim=1)[1] ## 获取最大值的索引[B,T] ## torch.max(A, dim=1)返回两个元素,分别是A在dim上的最大值(返回形状[B,T]),和这些最大值的在原本的维度dim上的索引(返回形状[B,T])。torch.max(A, dim=1)[1]表示取后者 max_value...
(method='bfill')combined_df["Total Equity"]=combined_df.sum(axis=1)returncombined_dfdefcalculate_metrics(equity_curve):"""Calculates total profit and maximum drawdown from the equity curve."""total_profit=equity_curve.iloc[-1]-equity_curve.iloc[0]peak=equity_curve.cummax()drawdown=peak-...