如何使用 Python pandas 更快地读取多个 CSV 文件

Question

我的程序应该读取 ~400.000 csv 文件，这需要很长时间。 我使用的代码是：

        for file in self.files:
            size=2048
            csvData = pd.read_csv(file, sep='\t', names=['acol', 'bcol'], header=None, skiprows=range(0,int(size/2)), skipfooter=(int(size/2)-10))

            for index in range(0,10):
                s=s+float(csvData['bcol'][index])
            s=s/10
            averages.append(s)
            time=file.rpartition('\\')[2]
            time=int(re.search(r'\d+', time).group())
            times.append(time)

有没有提高速度的机会？

Answer 1

您可以使用线程。 我从这里获取了以下代码并针对您的用例进行了修改

global times =[]

def my_func(file):
        size=2048
        csvData = pd.read_csv(file, sep='\t', names=['acol', 'bcol'], header=None, skiprows=range(0,int(size/2)), skipfooter=(int(size/2)-10))

        for index in range(0,10):
            s=s+float(csvData['bcol'][index])
        s=s/10
        averages.append(s)
        time=file.rpartition('\\')[2]
        time=int(re.search(r'\d+', time).group())
        times.append(time)

threads = []
# In this case 'self.files' is a list of files to be read.
for ii in range(self.files):
# We start one thread per file present.
    process = Thread(target=my_func, args=[ii])
    process.start()
    threads.append(process)
# We now pause execution on the main thread by 'joining' all of our started threads.
# This ensures that each has finished processing the urls.
for process in threads:
    process.join()