Question

我知道与其他语言相比，python循环本身相对较慢，但是当使用正确的函数时，它们会变得更快。我有一个名为“声学”的熊猫数据框，其中包含超过1000万行：

print(acoustics)
                        timestamp            c0  rowIndex
0        2016-01-01T00:00:12.000Z  13931.500000   8158791
1        2016-01-01T00:00:30.000Z  14084.099609   8158792
2        2016-01-01T00:00:48.000Z  13603.400391   8158793
3        2016-01-01T00:01:06.000Z  13977.299805   8158794
4        2016-01-01T00:01:24.000Z  13611.000000   8158795
5        2016-01-01T00:02:18.000Z  13695.000000   8158796
6        2016-01-01T00:02:36.000Z  13809.400391   8158797
7        2016-01-01T00:02:54.000Z  13756.000000   8158798

还有我写的代码：

acoustics = pd.read_csv("AccousticSandDetector.csv", skiprows=[1])
weights = [1/9, 1/18, 1/27, 1/36, 1/54]
sumWeights = np.sum(weights)
deltaAc = []
for i in range(5, len(acoustics)):
    time = acoustics.iloc[i]['timestamp']
    sum = 0
    for c in range(5):
        sum += (weights[c]/sumWeights)*(acoustics.iloc[i]['c0']-acoustics.iloc[i-c]['c0'])
    print("Row " + str(i) + " of " + str(len(acoustics)) + " is iterated")
    deltaAc.append([time, sum])

deltaAc = pd.DataFrame(deltaAc)

这需要花费大量时间，如何才能使其更快？

Answer 1

您可以使用pandas中的diff并为数组中的每一行创建所有差，然后将weigths乘以sum并沿轴1乘以，例如：

deltaAc = pd.DataFrame({'timestamp': acoustics.loc[5:, 'timestamp'], 
                       'summation': (np.array([acoustics.c0.diff(i) for i in range(5) ]).T[5:]
                                               *np.array(weights)).sum(1)/sumWeights})

您将获得与我的代码相同的值：

print (deltaAc)
                  timestamp  summation
5  2016-01-01T00:02:18.000Z -41.799986
6  2016-01-01T00:02:36.000Z  51.418728
7  2016-01-01T00:02:54.000Z  -3.111184

Answer 2

第一个优化，weights[c]/sumWeights可以在循环外完成。

weights_array = np.array([1/9, 1/18, 1/27, 1/36, 1/54])
sumWeights = np.sum(weights_array)
tmp = weights_array / sumWeights
...
        sum += tmp[c]*...

我对大熊猫不熟悉，但是如果您可以将列提取为一维numpy数组，那么这对您将非常有用。看起来可能像这样：

# next lines to be tested, or find the correct way of extracting the column
c0_column = acoustics[['c0']].values
time_column = acoustics[['times']].values
...
sum = numpy.zeros(shape=(len(acoustics)-5,))
delta_ac = []
for c in range(5):
    sum += tmp[c]*(c0_column[5:]-c0_column[5-c:len(acoustics)-c])

for i in range(len(acoustics)-5):
    deltaAc.append([time[5+i], sum[i])

Answer 3

数据帧有一个很棒的方法rolling，用于构造和应用窗口转换。因此，您根本不需要循环：

# df is your data frame
window_size = 5
weights = pd.np.array([1/9, 1/18, 1/27, 1/36, 1/54])
weights /= weights.sum()
df.loc[:,'deltaAc'] = df.loc[:, 'c0'].rolling(window_size).apply(lambda x: ((x[-1] - x)*weights).sum())

如何使此for循环更快？

3 个答案: