• numexpr: for accelerating certain numerical operations. numexpr uses multiple cores as well as smart chunk- ing and caching to achieve large speedups. • bottleneck: for accelerating certain types of are especially useful when dealing with large data sets, and provide large speedups. numexpr uses smart chunking, caching, and multiple cores. bottleneck is a set of specialized cython routines that are 417952 dtype: float64 Now imagine you want to compare GDP to the share of people with cellphone contracts around the world. In [7]: wb.search(’cell.*%’).iloc[:,:2] Out[7]: id name 3990 IT.CEL.SETS.FE

• numexpr: for accelerating certain numerical operations. numexpr uses multiple cores as well as smart chunk- ing and caching to achieve large speedups. • bottleneck: for accelerating certain types of are especially useful when dealing with large data sets, and provide large speedups. numexpr uses smart chunking, caching, and multiple cores. bottleneck is a set of specialized cython routines that are 417952 dtype: float64 Now imagine you want to compare GDP to the share of people with cellphone contracts around the world. In [7]: wb.search(’cell.*%’).iloc[:,:2] Out[7]: id name 3990 IT.CEL.SETS.FE

• numexpr: for accelerating certain numerical operations. numexpr uses multiple cores as well as smart chunk- ing and caching to achieve large speedups. If installed, must be Version 2.1 or higher. • are especially useful when dealing with large data sets, and provide large speedups. numexpr uses smart chunking, caching, and multiple cores. bottleneck is a set of specialized cython routines that are 417952 dtype: float64 Now imagine you want to compare GDP to the share of people with cellphone contracts around the world. In [7]: wb.search(’cell.*%’).iloc[:,:2] Out[7]: id name 3990 IT.CEL.SETS.FE

• numexpr: for accelerating certain numerical operations. numexpr uses multiple cores as well as smart chunk- ing and caching to achieve large speedups. If installed, must be Version 2.1 or higher. • are especially useful when dealing with large data sets, and provide large speedups. numexpr uses smart chunking, caching, and multiple cores. bottleneck is a set of specialized cython routines that are 417952 dtype: float64 Now imagine you want to compare GDP to the share of people with cellphone contracts around the world. In [7]: wb.search(’cell.*%’).iloc[:,:2] Out[7]: id name 3990 IT.CEL.SETS.FE

• numexpr: for accelerating certain numerical operations. numexpr uses multiple cores as well as smart chunk- ing and caching to achieve large speedups. If installed, must be Version 2.1 or higher. • are especially useful when dealing with large data sets, and provide large speedups. numexpr uses smart chunking, caching, and multiple cores. bottleneck is a set of specialized cython routines that are 417952 dtype: float64 Now imagine you want to compare GDP to the share of people with cellphone contracts around the world. In [7]: wb.search('cell.*%').iloc[:,:2] Out[7]: id name 3990 IT.CEL.SETS.FE

• numexpr: for accelerating certain numerical operations. numexpr uses multiple cores as well as smart chunk- ing and caching to achieve large speedups. If installed, must be Version 2.6.2 or higher. are especially useful when dealing with large data sets, and provide large speedups. numexpr uses smart chunking, caching, and multiple cores. bottleneck is a set of specialized cython routines that are

• numexpr: for accelerating certain numerical operations. numexpr uses multiple cores as well as smart chunk- ing and caching to achieve large speedups. • bottleneck: for accelerating certain types of are especially useful when dealing with large data sets, and provide large speedups. numexpr uses smart chunking, caching, and multiple cores. bottleneck is a set of specialized cython routines that are

• numexpr: for accelerating certain numerical operations. numexpr uses multiple cores as well as smart chunk- ing and caching to achieve large speedups. If installed, must be Version 2.1 or higher (excluding are especially useful when dealing with large data sets, and provide large speedups. numexpr uses smart chunking, caching, and multiple cores. bottleneck is a set of specialized cython routines that are

• numexpr: for accelerating certain numerical operations. numexpr uses multiple cores as well as smart chunk- ing and caching to achieve large speedups. If installed, must be Version 2.1 or higher (excluding are especially useful when dealing with large data sets, and provide large speedups. numexpr uses smart chunking, caching, and multiple cores. bottleneck is a set of specialized cython routines that are

• numexpr: for accelerating certain numerical operations. numexpr uses multiple cores as well as smart chunk- ing and caching to achieve large speedups. If installed, must be Version 2.4.6 or higher. are especially useful when dealing with large data sets, and provide large speedups. numexpr uses smart chunking, caching, and multiple cores. bottleneck is a set of specialized cython routines that are