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README.md

@@ -64,7 +64,7 @@ Each program has three identical parts. First it generates and populates 3 colum
 The maximum dataset I could load into Polars was 300m rows per column. Any bigger dataset blew up the memory and caused OS to kill it. I ran C++ DataFrame with 10b rows per column and I am sure it would have run with bigger datasets too. So, I was forced to run both with 300m rows to compare.
 I ran each test 4 times and took the best time. Polars numbers varied a lot from one run to another, especially calculation and selection times. C++ DataFrame numbers were significantly more consistent.
 
-|                          | [<B>C++ DataFrame</B>](https://github.com/hosseinmoein/DataFrame/blob/master/benchmarks/dataframe_performance.cc) | [<B>Polars</B>](https://github.com/hosseinmoein/DataFrame/blob/master/benchmarks/polars_performance.py) | [<B>Pandas</B>](https://github.com/hosseinmoein/DataFrame/blob/master/benchmarks/pandas_performance.py) |
+|                          | [<B>C++ DataFrame</B>](https://github.com/hosseinmoein/DataFrame/blob/master/benchmarks/dataframe_performance.cc) | [<B>Polars   </B>](https://github.com/hosseinmoein/DataFrame/blob/master/benchmarks/polars_performance.py) | [<B>Pandas   </B>](https://github.com/hosseinmoein/DataFrame/blob/master/benchmarks/pandas_performance.py) |
 |            :--           |        ---:         |     ---:      |     ---:      |
 | Data generation/load time | 26.9459 secs | 28.4686 secs | 36.6799 secs |
 | Calculation time | 1.2602 secs | 4.8766 secs | 40.3264 secs |