def calculate_average(numbers): if not numbers: # 检查列表是否为空 return None total = sum(numbers) # 计算总和 average = total / len(numbers) # 计算平均值 return average # 示例使用 numbers_list = [10, 20, 30, 40, 50] print("平均值是:", calculate_average(numbers_list)) 可能遇到的...
To achieve this, we have to specify a list of group columns within the groupby function.Consider the Python syntax below:print(data.groupby(['group1', 'group2']).mean()) # Get mean by multiple groups # x1 x2 # group1 group2 # A a 4.5 12.0 # b 8.0 18.0 # B a 5.0 12.0 # ...
100)) In [4]: roll = df.rolling(100) # 默认使用单Cpu进行计算 In [5]: %timeit roll.mean(engine="numba", engine_kwargs={"parallel": True}) 347 ms ± 26 ms per loop (mean ± std. dev. of 7 runs, 1 loop each) # 设置使用2个CPU进行并行计算,...
def calculate_and print_stats(list_of_numbers):sum = sum(list_of_numbers) mean = statistics.mean(list_of_numbers) median = statistics.median(list_of_numbers) mode = statistics.mode(list_of_numbers) print('---Stats---') print('SUM: {}'.format(sum) print('MEAN: {}'...
# Group by a column and calculate mean for each groupgrouped = df.groupby('group_column')['value_column'].mean() 分组和汇总数据对于汇总数据集中的信息至关重要。你可以使用Pandas的groupby方法计算每个组的统计数据。透视表 # Create a pivot tablepi...
mean1=np.mean(group) for i in group: error=i-mean1 se+=error**2 return se ''' >>> SE(group1) 22.0 >>> SE(group2) 18.0 >>> SE(group3) 14.0 ''' #sum of squares within group error,also know as random error def SSE(list_groups): ...
set_title("Slope of Trench") plt.show() del slope_image plt.clf() 总结 步骤4 基于正负地形和基本地貌类型,提取到了沟坡和沟底的分布范围。 步骤五 沟坡覆盖度计算 统计沟坡面积 统计流域面积 计算面积比例 5.1 统计沟坡面积 基于流域单元,统计每个流域的沟坡面积。 参数说明: stat:统计方式,包含 Mean...
history = [x for x in train]# make predictionspredictions = list()for t in range(len(test)):model = ARIMA(history, order=arima_order)model_fit = model.fit(disp=0)yhat = model_fit.forecast()[0]predictions.append(yhat)history.append(test[t])# calculate out of sample errormse = mean...
number_of_games = int(1e6) outcomes = roulette(number_of_games) payoffs = payoff(outcomes) 使用np.mean函数计算赔偿向量的平均值。你得到的值应该接近-0.027027:np.mean(payoffs) 负数意味着平均每下注一单位就会损失-0.027027。请记住,你的损失就是赌场的利润。这是他们的生意。在这个练习中,我们学会了...
Mean of 'n' Nearest Past Neighbors: 使用'k'个最近的过去邻居的均值来填充缺失值,并计算填充后数据与原始数据的均方误差(MSE)。 Seasonal Mean: 使用相应季节期间的均值,它计算了对应季节期间的均值,并使用该均值来填充缺失值。然后,计算填充后数据与原始数据的均方误差(MSE),并绘制填充后的数据与原始数据的比较...