analysis you will hardly notice the difference (because reindex has been heavily optimized), but when CPU cycles matter sprinkling a few explicit reindex calls here and there can have an impact. Reindexing or statically (using the included pycc tool). Numba supports compilation of Python to run on either CPU or GPU hardware, and is designed to integrate with the Python scientific software stack. Note: You ..: counts = counts.add(df['name'].value_counts(), fill_value=0) ....: counts.astype(int) ....: CPU times: user 2.19 s, sys: 329 ms, total: 2.52 s Wall time: 2.38 s Out[19]: Alice 229802 Bob 229211

analysis you will hardly notice the difference (because reindex has been heavily optimized), but when CPU cycles matter sprinkling a few explicit reindex calls here and there can have an impact. Reindexing or statically (using the included pycc tool). Numba supports compilation of Python to run on either CPU or GPU hardware, and is designed to integrate with the Python scientific software stack. Note: You ..: counts = counts.add(df['name'].value_counts(), fill_value=0) ....: counts.astype(int) ....: CPU times: user 2.62 s, sys: 188 ms, total: 2.81 s Wall time: 2.46 s Out[19]: Alice 229802 Bob 229211

analysis you will hardly notice the difference (because reindex has been heavily optimized), but when CPU cycles matter sprinkling a few explicit reindex calls here and there can have an impact. Reindexing or statically (using the included pycc tool). Numba supports compilation of Python to run on either CPU or GPU hardware, and is designed to integrate with the Python scientific software stack. Note: You ..: counts = counts.add(df['name'].value_counts(), fill_value=0) ....: counts.astype(int) ....: CPU times: user 1.53 s, sys: 297 ms, total: 1.83 s Wall time: 1.89 s Out[19]: Alice 229802 Bob 229211

analysis you will hardly notice the difference (because reindex has been heavily optimized), but when CPU cycles matter sprinkling a few explicit reindex calls here and there can have an impact. Reindexing or statically (using the included pycc tool). Numba supports compilation of Python to run on either CPU or GPU hardware, and is designed to integrate with the Python scientific software stack. Note: You ..: counts = counts.add(df['name'].value_counts(), fill_value=0) ....: counts.astype(int) ....: CPU times: user 2.31 s, sys: 245 ms, total: 2.55 s Wall time: 2.92 s Out[19]: Alice 229802 Bob 229211

analysis you will hardly notice the difference (because reindex has been heavily optimized), but when CPU cycles matter sprinkling a few explicit reindex calls here and there can have an impact. Reindexing or statically (using the included pycc tool). Numba supports compilation of Python to run on either CPU or GPU hardware, and is designed to integrate with the Python scientific software stack. Note: You ..: counts = counts.add(df['name'].value_counts(), fill_value=0) ....: counts.astype(int) ....: CPU times: user 1.62 s, sys: 246 ms, total: 1.87 s Wall time: 2.24 s Out[19]: Alice 229802 Bob 229211

analysis you will hardly notice the difference (because reindex has been heavily optimized), but when CPU cycles matter sprinkling a few explicit reindex calls here and there can have an impact. Reindexing or statically (using the included pycc tool). Numba supports compilation of Python to run on either CPU or GPU hardware, and is designed to integrate with the Python scientific software stack. Note: You ..: counts = counts.add(df['name'].value_counts(), fill_value=0) ....: counts.astype(int) ....: CPU times: user 1.63 s, sys: 263 ms, total: 1.89 s Wall time: 1.79 s Out[19]: Alice 229802 Bob 229211

analysis you will hardly notice the difference (because reindex has been heavily optimized), but when CPU cycles matter sprinkling a few explicit reindex calls here and there can have an impact. Reindexing or statically (using the included pycc tool). Numba supports compilation of Python to run on either CPU or GPU hardware, and is designed to integrate with the Python scientific software stack. Note: You ..: counts = counts.add(df["name"].value_counts(), fill_value=0) ....: counts.astype(int) ....: CPU times: user 670 ms, sys: 133 ms, total: 803 ms Wall time: 603 ms Out[19]: Alice 229802 Bob 229211

analysis you will hardly notice the difference (because reindex has been heavily optimized), but when CPU cycles matter sprinkling a few explicit reindex calls here and there can have an impact. Reindexing or statically (using the included pycc tool). Numba supports compilation of Python to run on either CPU or GPU hardware and is designed to integrate with the Python scientific software stack. Note: The ..: counts = counts.add(df["name"].value_counts(), fill_value=0) ....: counts.astype(int) ....: CPU times: user 679 ms, sys: 48.4 ms, total: 727 ms Wall time: 484 ms Out[19]: Alice 229802 Bob 229211

analysis you will hardly notice the difference (because reindex has been heavily optimized), but when CPU cycles matter sprinkling a few explicit reindex calls here and there can have an impact. Reindexing or statically (using the included pycc tool). Numba supports compilation of Python to run on either CPU or GPU hardware and is designed to integrate with the Python scientific software stack. Note: The ..: counts = counts.add(df["name"].value_counts(), fill_value=0) ....: counts.astype(int) ....: CPU times: user 576 ms, sys: 48.1 ms, total: 624 ms Wall time: 432 ms Out[19]: Alice 229802 Bob 229211

analysis you will hardly notice the difference (because reindex has been heavily optimized), but when CPU cycles matter sprinkling a few explicit reindex calls here and there can have an impact. Reindexing or statically (using the included pycc tool). Numba supports compilation of Python to run on either CPU or GPU hardware and is designed to integrate with the Python scientific software stack. Note: The ..: counts = counts.add(df["name"].value_counts(), fill_value=0) ....: counts.astype(int) ....: CPU times: user 614 ms, sys: 17.9 ms, total: 632 ms Wall time: 445 ms Out[19]: Alice 229802 Bob 229211