Python：从 API 列表计算指标

Question

我有一个看起来像这样的数据数组：

#API name, min, max, average
['findProductByPartNumber', '336.0', '336.0', '336.0']
['findProductByPartNumber', '336.0', '339.0', '337.5']
['findProductByPartNumber', '336.0', '339.0', '338.0']
['findProductByPartNumber', '336.0', '341.0', '338.75']
['findProductByPartNumber', '336.0', '353.0', '341.6']
['findProductById', '841.0', '841.0', '841.0']
['findProductByPartNumber', '336.0', '920.0', '438.0']
['findProductByPartNumber', '336.0', '944.0', '510.29']
['findProductByPartNumber', '336.0', '952.0', '565.5']
['findProductByPartNumber', '336.0', '975.0', '611.0']
['findProductsByCategory', '113.0', '113.0', '113.0']
['findProductById', '161.0', '841.0', '501.0']
['findProductByPartNumber', '255.0', '975.0', '575.4']

我想做的是，对于每个单独的 API，生成如下内容：

API, Min, Max, Average, 90th Percentile
findProductByPartNumber, 278.69, 770.25, 458.69, 565.5
findProductById, 373.0, 841.0, 571.67, 501.0
findProductsByCategory, 112.33, 187.17, 154.46, 167.75

这是上述每个 API 的汇总结果。在 Python 中执行此操作的最佳方法是什么？

编辑：

我有以下 Java 代码可以满足我的需求。 Java 是我最好的语言，我正在尝试学习 Python，但我不熟悉数据结构。

double[] apiValues = new double[3];
apiValues[0] = Double.valueOf(min);
apiValues[1] = Double.valueOf(max);
apiValues[2] = Double.valueOf(average);
parseAPILogs.registerAPI(name, apiValues);
...
...
private static void registerAPI(String apiName, double[] apiValues) {
    if(!averagePerAPI.containsKey(apiName)) {
        APIData data = new APIData();
        data.addValues(apiValues);
        averagePerAPI.put(apiName, data);
    } else {
        averagePerAPI.get(apiName).addValues(apiValues);
}
}

APIData Java 类在这里有点大，但你可以看到我的想法。

Answer 1

这样的事情怎么样：

def aggregate(stats):
    aggregated = {}
    for stat in stats:
        key = stat.pop(0)
        stat = map(float, stat)
        if key not in aggregated:
            vals = {"avg": []}
            aggregated[key] = vals
        aggregated[key]['min'] = min(stat[0], aggregated[key].setdefault('min', stat[0]))
        aggregated[key]['max'] = max(stat[1], aggregated[key].setdefault('max', stat[1]))
        aggregated[key]['avg'].append(stat[2])
    return aggregated

def print_stats(aggregated):
    for k, v in aggregated.items():
        print k, 
        for k1, v1 in v.items():
            if k1 == 'avg':
                print "%s: %s" % (k1, sum(v1) / len(v1)),
            else:
                print "%s: %s" % (k1, v1),
        print

 stats = [
        ['findProductByPartNumber', '336.0', '336.0', '336.0'],
        ['findProductByPartNumber', '336.0', '339.0', '337.5'],
        ['findProductByPartNumber', '336.0', '339.0', '338.0'],
        ['findProductByPartNumber', '336.0', '341.0', '338.75'],
        ['findProductByPartNumber', '336.0', '353.0', '341.6'],
        ['findProductById', '841.0', '841.0', '841.0'],
        ['findProductByPartNumber', '336.0', '920.0', '438.0'],
        ['findProductByPartNumber', '336.0', '944.0', '510.29'],
        ['findProductByPartNumber', '336.0', '952.0', '565.5'],
        ['findProductByPartNumber', '336.0', '975.0', '611.0'],
        ['findProductsByCategory', '113.0', '113.0', '113.0'],
        ['findProductById', '161.0', '841.0', '501.0'],
        ['findProductByPartNumber', '255.0', '975.0', '575.4']
        ]   

print_stats(aggregate(stats))

输出

findProductsByCategory max: 113.0 avg: 113.0 min: 113.0
findProductById max: 841.0 avg: 671.0 min: 161.0
findProductByPartNumber max: 975.0 avg: 439.204 min: 255.0

就第 90 个百分位数而言，除非您仅使用 .9 * avg，否则无法在没有单个样本的情况下进行计算。

Answer 2

arr = [ ['findProductByPartNumber', '336.0', '336.0', '336.0'],
        ['findProductByPartNumber', '336.0', '339.0', '337.5'],
        ['findProductByPartNumber', '336.0', '339.0', '338.0'],
        ['findProductByPartNumber', '336.0', '341.0', '338.75'],
        ['findProductByPartNumber', '336.0', '353.0', '341.6'],
        ['findProductById', '841.0', '841.0', '841.0'],
        ['findProductByPartNumber', '336.0', '920.0', '438.0'],
        ['findProductByPartNumber', '336.0', '944.0', '510.29'],
        ['findProductByPartNumber', '336.0', '952.0', '565.5'],
        ['findProductByPartNumber', '336.0', '975.0', '611.0'],
        ['findProductsByCategory', '113.0', '113.0', '113.0'],
        ['findProductById', '161.0', '841.0', '501.0'],
        ['findProductByPartNumber', '255.0', '975.0', '575.4']]

d = list(set([item[0] for item in arr]))
d = dict(zip(d, [[0, 0, 0, 0] for k in range(len(d))]))

for k in arr:
    d[k[0]][0] = min(k[1], d[k[0]][0]) if k[1] is 0 else k[1]
    d[k[0]][1] = max(k[2], d[k[0]][1])
    d[k[0]][2] = sum(map(lambda x: float(x[3]) if x[0] is k[0] else 0, [api for api in arr])) / len(filter(lambda x: x is k[0], [api[0] for api in arr]))

for k in d.keys():
    print "{0} {1} {2} {3} {4}".format(k, d[k][0], d[k][1], d[k][2], d[k][3])