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User guide numpy NumPy User Guide Release Written by the NumPy community July C CCONTENTS Introduction What is NumPy Building and installing NumPy How to ?nd documentation Numpy basics Data types Array creation I O with Numpy Indexing Broadcasting Byte-sw

NumPy User Guide Release Written by the NumPy community July C CCONTENTS Introduction What is NumPy Building and installing NumPy How to ?nd documentation Numpy basics Data types Array creation I O with Numpy Indexing Broadcasting Byte-swapping Structured arrays aka ??Record arrays ? Subclassing ndarray Performance Miscellaneous IEEE Floating Point Special Values How numpy handles numerical exceptions Examples Interfacing to C Interfacing to Fortran Interfacing to C Methods vs Functions Using Numpy C-API How to extend NumPy Using Python as glue Beyond the Basics Python Module Index Index i Cii CNumPy User Guide Release This guide is intended as an introductory overview of NumPy and explains how to install and make use of the most important features of NumPy For detailed reference documentation of the functions and classes contained in the package see the reference Warning This ??User Guide ? is still a work in progress some of the material is not organized and several aspects of NumPy are not yet covered suf ?cient detail We are an open source community continually working to improve the documentation and eagerly encourage interested parties to contribute For information on how to do so please visit the NumPy doc wiki More documentation for NumPy can be found on the numpy org website Thanks CONTENTS CNumPy User Guide Release CONTENTS CCHAPTER ONE INTRODUCTION What is NumPy NumPy is the fundamental package for scienti ?c computing in Python It is a Python library that provides a multidimensional array object various derived objects such as masked arrays and matrices and an assortment of routines for fast operations on arrays including mathematical logical shape manipulation sorting selecting I O discrete Fourier transforms basic linear algebra basic statistical operations random simulation and much more At the core of the NumPy package is the ndarray object This encapsulates n- dimensional arrays of homogeneous data types with many operations being performed in compiled code for performance There are several important di ?erences between NumPy arrays and the standard Python sequences ? NumPy arrays have a ?xed size at creation unlike Python lists which can grow dynamically Changing the size of an ndarray will create a new array and delete the original ? The elements in a NumPy array are all required to be of the same data type and thus will be the same size in memory The exception one can have arrays of Python including NumPy objects thereby allowing for arrays of di ?erent sized elements ? NumPy arrays facilitate advanced mathematical and other types of operations on large numbers of data Typically such operations are executed more ef ?ciently and with less code than is possible using Python ? s built-in sequences ? A growing plethora of scienti ?c and mathematical Python-based packages are using NumPy arrays though these typically support Python-sequence input they convert such input to NumPy arrays prior to processing and they often output NumPy arrays In other words in order to ef ?ciently use much perhaps even most of today ? s scienti ?c