擅长:python、mysql、java
<p>因此,使用Pandas(在Python中处理数据帧的库)可以执行如下操作,这比像您这样手动处理CSV要快得多:</p>
<pre><code>import pandas as pd
csvfile = "C:/Users/Simon/Desktop/test.csv"
data = pd.read_csv(csvfile, header=None, names=['time','cpu','energy'])
times = list(pd.unique(data.time.ravel()))
print times
cpuList = data.groupby(['cpu'])
cpuEnergy = {}
for i in range(len(cpuList)):
curCPU = 'CPU' + str(i+1)
cpuEnergy[curCPU] = list(cpuList.get_group('CPU' + str(i+1))['energy'])
for k, v in cpuEnergy.items():
print k, v
</code></pre>
<p>输出如下:</p>
<pre><code>[0.50018755000000004, 1.000445359, 1.5006748769999998]
CPU4 [1.6200000000000001, 1.6100000000000001, 1.6100000000000001]
CPU2 [1.6200000000000001, 1.6100000000000001, 1.6100000000000001]
CPU3 [7.9299999999999997, 9.9600000000000009, 9.9399999999999995]
CPU1 [7.9299999999999997, 9.9600000000000009, 9.9399999999999995]
</code></pre>