introduction numpy (pronounced num-pie and acronym for 'numerical pythonor 'numeric python'is popular scientific library for python that gives developeracademicor scientist tools to work with high-level mathematical functions as well as multi-dimensional arrays and matrices it is blessing for integrating cc+and fortran tools it also provides numerous functions for fourier transform (ftand linear algebra python has an integer typea float typeand complex typehoweverthis is not enough for scientific computing andfor this reasonnumpy has lot more data types with varying precisiondependent on memory requirements the built-in data types of python discussed in the previous part are powerful and flexible enough for many programming tasks howeverin sciencewe often want to do computations on collection of numerical data suppose we have five participantsfor each of which we have an age and weightand we want to compute their body mass index (bmiin matlabwe can use vector of weights and vector of length and operate on thoseelement-wise the closest thing to vector in python would be howeverthe data type does not allow such element-wise operations as you might rememberoperators such as '+and '*perform concatenations and replications of listswhereas operators such as '/and '-do not work at allthe numpy package is the workhorse of data analysismachine learningand scientific computing in the python ecosystem it vastly simplifies manipulating and crunching vectors and matrices some of python' leading package relies on numpy as fundamental piece of their infrastructure (examples include scikitlearnscipypandasand tensorflowbeyond the ability to slice and dice numeric datamastering numpy will give you an edge when dealing and debugging with advanced use cases in these libraries operations using numpy using numpya developer can perform the following operations mathematical and logical operations on arrays fourier transforms and routines for shape manipulation operations related to linear algebra numpy has in-built functions for linear algebra and random number generation why numpy instead of listsone might think of why one should prefer arrays in numpy instead we can create lists having the same data type if this statement also rings bell then the following reasons may convince youpage