Performance

openpyxl attempts to balance functionality and performance. Where in doubt, we have focused on functionality over optimisation: performance tweaks are easier once an API has been established. Memory use is fairly high in comparison with other libraries and applications and is approximately 50 times the original file size, e.g. 2.5 GB for a 50 MB Excel file. As many use cases involve either only reading or writing files, the Optimised Modes modes mean this is less of a problem.

Benchmarks

All benchmarks are synthetic and extremely dependent upon the hardware but they can nevertheless give an indication.

Write Performance

The benchmark code can be adjusted to use more sheets and adjust the proportion of data that is strings. Because the version of Python being used can also significantly affect performance, a driver script can also be used to test with different Python versions with a tox environment.

Performance is compared with the excellent alternative library xlsxwriter


Versions:
python: 2.7.1
openpyxl: 2.6.0dev
xlsxwriter: 1.0.9

Dimensions:
    Rows = 1000
    Cols = 50
    Sheets = 4
    Proportion text = 0.10

Times:
    xlsxwriter            :   2.45
    xlsxwriter (optimised):   2.64
    openpyxl              :   3.96
    openpyxl (optimised)  :   2.78


Versions:
python: 3.5.6
openpyxl: 2.6.0dev
xlsxwriter: 1.0.9

Dimensions:
    Rows = 1000
    Cols = 50
    Sheets = 4
    Proportion text = 0.10

Times:
    xlsxwriter            :   2.29
    xlsxwriter (optimised):   2.22
    openpyxl              :   4.35
    openpyxl (optimised)  :   2.90


Versions:
python: 3.6.6
openpyxl: 2.6.0dev
xlsxwriter: 1.0.9

Dimensions:
    Rows = 1000
    Cols = 50
    Sheets = 4
    Proportion text = 0.10

Times:
    xlsxwriter            :   2.32
    xlsxwriter (optimised):   2.22
    openpyxl              :   3.35
    openpyxl (optimised)  :   2.64


Versions:
python: 3.7.0
openpyxl: 2.6.0dev
xlsxwriter: 1.0.9

Dimensions:
    Rows = 1000
    Cols = 50
    Sheets = 4
    Proportion text = 0.10

Times:
    xlsxwriter            :   2.34
    xlsxwriter (optimised):   2.23
    openpyxl              :   2.93
    openpyxl (optimised)  :   2.49

Read Performance

Performance is measured using a file provided with a previous bug report and compared with the older xlrd library. xlrd is primarily for the older BIFF file format of .XLS files but it does have limited support for XLSX.

The code for the benchmark shows the importance of choosing the right options when working with a file. In this case disabling external links stops openpyxl opening cached copies of the linked worksheets.

One major difference between the libraries is that openpyxl’s read-only mode opens a workbook almost immediately making it suitable for multiple processes, this also readuces memory use significantly. xlrd does also not automatically convert dates and times into Python datetimes, though it does annotate cells accordingly but to do this in client code significantly reduces performance.


Versions:
python: 2.7.1
xlread: 1.1.0
openpyxl: 2.6.0dev

xlrd
    Workbook loaded 66.72s
    OptimizationData 0.19s
    Output Model 0.00s
    >>DATA>> 0.00s
    Store days 0% 0.07s
    Store days 100% 0.06s
    Total time 67.04s

openpyxl
    Workbook loaded 106.64s
    OptimizationData 0.00s
    Output Model 0.00s
    >>DATA>> 0.00s
    Store days 0% 0.00s
    Store days 100% 0.00s
    Total time 106.64s

openpyxl, read-only
    Workbook loaded 0.97s
    OptimizationData 24.77s
    Output Model 0.00s
    >>DATA>> 0.00s
    Store days 0% 24.19s
    Store days 100% 19.18s
    Total time 69.11s

openpyxl, read-only, values only
    Workbook loaded 0.95s
    OptimizationData 21.84s
    Output Model 0.00s
    >>DATA>> 0.00s
    Store days 0% 21.94s
    Store days 100% 17.09s
    Total time 61.82s


Versions:
python: 3.5.6
xlread: 1.1.0
openpyxl: 2.6.0dev

xlrd
    Workbook loaded 67.13s
    OptimizationData 0.24s
    Output Model 0.00s
    >>DATA>> 0.00s
    Store days 0% 0.08s
    Store days 100% 0.07s
    Total time 67.52s

openpyxl
    Workbook loaded 115.50s
    OptimizationData 0.00s
    Output Model 0.00s
    >>DATA>> 0.00s
    Store days 0% 0.00s
    Store days 100% 0.00s
    Total time 115.50s

openpyxl, read-only
    Workbook loaded 1.25s
    OptimizationData 38.46s
    Output Model 0.00s
    >>DATA>> 0.00s
    Store days 0% 29.54s
    Store days 100% 22.78s
    Total time 92.04s

openpyxl, read-only, values only
    Workbook loaded 1.30s
    OptimizationData 27.08s
    Output Model 0.00s
    >>DATA>> 0.00s
    Store days 0% 27.09s
    Store days 100% 21.13s
    Total time 76.59s


Versions:
python: 3.6.7
xlread: 1.1.0
openpyxl: 2.6.0dev

xlrd
    Workbook loaded 52.04s
    OptimizationData 0.23s
    Output Model 0.00s
    >>DATA>> 0.00s
    Store days 0% 0.08s
    Store days 100% 0.07s
    Total time 52.42s

openpyxl
    Workbook loaded 91.79s
    OptimizationData 0.00s
    Output Model 0.00s
    >>DATA>> 0.00s
    Store days 0% 0.00s
    Store days 100% 0.00s
    Total time 91.79s

openpyxl, read-only
    Workbook loaded 1.08s
    OptimizationData 25.53s
    Output Model 0.00s
    >>DATA>> 0.00s
    Store days 0% 23.02s
    Store days 100% 17.97s
    Total time 67.61s

openpyxl, read-only, values only
    Workbook loaded 1.08s
    OptimizationData 20.90s
    Output Model 0.01s
    >>DATA>> 0.00s
    Store days 0% 21.05s
    Store days 100% 16.15s
    Total time 59.20s


Versions:
python: 3.7.1
xlread: 1.1.0
openpyxl: 2.6.0dev

xlrd
    Workbook loaded 49.78s
    OptimizationData 0.22s
    Output Model 0.00s
    >>DATA>> 0.00s
    Store days 0% 0.07s
    Store days 100% 0.06s
    Total time 50.13s

openpyxl
    Workbook loaded 88.81s
    OptimizationData 0.00s
    Output Model 0.00s
    >>DATA>> 0.00s
    Store days 0% 0.00s
    Store days 100% 0.00s
    Total time 88.81s

openpyxl, read-only
    Workbook loaded 0.94s
    OptimizationData 21.73s
    Output Model 0.00s
    >>DATA>> 0.00s
    Store days 0% 24.94s
    Store days 100% 17.21s
    Total time 64.82s

openpyxl, read-only, values only
    Workbook loaded 0.97s
    OptimizationData 19.94s
    Output Model 0.00s
    >>DATA>> 0.00s
    Store days 0% 19.88s
    Store days 100% 15.42s
    Total time 56.20s

Parallelisation

Reading worksheets is fairly CPU-intensive which limits any benefits to be gained by parallelisation. However, if you are mainly interested in dumping the contents of a workbook then you can use openpyxl’s read-only mode and open multiple instances of a workbook and take advantage of multiple CPUs.

Sample code using the same source file as for read performance shows that performance scales reasonably with only a slight overhead due to creating additional Python processes.