analysis you will hardly notice the difference (because reindex has been heavily optimized), but when CPU cycles matter sprinking a few explicit reindex calls here and there can have an impact. 6.6.1 Reindexing

analysis you will hardly notice the difference (because reindex has been heavily optimized), but when CPU cycles matter sprinking a few explicit reindex calls here and there can have an impact. 6.6.1 Reindexing

analysis you will hardly notice the difference (because reindex has been heavily optimized), but when CPU cycles matter sprinking a few explicit reindex calls here and there can have an impact. 6.6.1 Reindexing

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: The the largest performance gain can be realized by setting parallel to True to leverage more than 1 CPU. Internally, pandas leverages numba to parallelize computations over the columns of a DataFrame; therefore