pandas to_excel-如何使其更快

问题描述

我有一个包含12,000行和34列的数据框.熊猫大约需要15秒才能将此内容写入Excel.我读到的关于to_excel函数的讨论很少，而使其更快的一种方法是添加engine ='xlsxwriter'.我使用以下代码.

I have a dataframe with 12,000 rows and 34 columns. It takes around 15 sec for pandas to write this to the excel. I read few discussion about to_excel function and one way to make it faster is by adding engine='xlsxwriter'. I use the following code.

writer = pd.ExcelWriter('outputfile.xlsx',engine='xlsxwriter')
res_df.to_excel(writer,sheet_name='Output_sheet')

想知道是否有一种方法可以使用dask或其他任何库来使这项工作更快?

Wondering if there is a way to make this work faster using dask or any other library?

dataframe.memory_usage()给了我以下输出:

dataframe.memory_usage() gave me the following output:

Index   80
col1    95528
col2    95528
col3    95528
col4    95528
col5    95528
col6    95528
col7    95528
col8    95528
col9    95528
col10   95528
col11   95528
col12   95528
col13   95528
col14   95528
col15   95528
col16   95528
col17   95528
col18   95528
col19   95528
col20   95528
col21   95528
col22   95528
col23   95528
col24   95528
col25   95528
col26   95528
col27   95528
col28   95528
col29   95528
col30   95528
col31   95528
col32   95528
col33   95528
col34   95528

谢谢！

推荐答案

您可以使用 pyexcelerate 来获取更快的速度.

You can use pyexcelerate to get a much faster speed.

from pyexcelerate import Workbook

values = [res_df.columns] + list(res_df.values)
wb = Workbook()
wb.new_sheet('sheet name', data=values)
wb.save('outputfile.xlsx')

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