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README.md

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@@ -11,7 +11,7 @@ The Python utilities also gives you the quickest possible way to export a Pandas
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## Python
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### Installing
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```
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```bash
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$ pip install py-excel-rs
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```
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TODO: Add rust documentation
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## Benchmarks
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With a focus on squeezing out as much performance as possible, **py-excel-rs** is up to **65.5x** faster than `pandas` and **17.5x** faster than the next fastest `xlsx` writer on pip.
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With a focus on squeezing out as much performance as possible, **py-excel-rs** is up to **45.5x** faster than `pandas` and **12.5x** faster than the fastest `xlsx` writer on pip.
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**cli-excel-rs** also managed to out perform [csv2xlsx](https://github.com/mentax/csv2xlsx?tab=readme-ov-file), the most poopular csv to xlsx tool. It is up to **12x** faster given the same dataset.
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These tests used a sample dataset from [DataBlist](https://www.datablist.com/learn/csv/download-sample-csv-files) that contained 1,000,000 rows and 9 columns.
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### Python
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#### py-excel-rs (2.89s)
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```
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```bash
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$ time python test-py-excel-rs.py
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python3 test-py-excel-rs.py 2.00s user 0.18s system 99% cpu 2.892 total
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```
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#### openpyxl (97.38s)
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```
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```bash
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$ time python test-openpyxl.py
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python3 test-openpyxl.py 94.48s user 2.39s system 99% cpu 1:37.38 total
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```
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#### pandas `to_excel()` (131.24s)
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```
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```bash
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$ time python test-pandas.py
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python3 test-pandas.py 127.99s user 2.75s system 99% cpu 2:11.24 total
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```
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#### pandas `to_excel(engine="xlsxwriter")` (82.29s)
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```
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```bash
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$ time python test-pandas-xlsxwriter.py
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python3 test-pandas-xlsxwriter.py 76.86s user 1.95s system 95% cpu 1:22.29 total
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```
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#### xlsxwriter (42.543s)
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```
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```bash
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$ time python test-xlsxwriter.py
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python3 test-xlsxwriter.py 41.58s user 0.81s system 99% cpu 42.543 total
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```
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#### pyexcelerate (35.821s)
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```
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```bash
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$ time python test-pyexcelerate.py
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python3 test-pyexcelerate.py 35.27s user 0.33s system 99% cpu 35.821 total
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```

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