Python chapter 04 numpy Basics: array and vector calculation
編輯：Python
NumPy（Numerical Python For short ） yes Python Numerical calculation of the most important base package . Most of the packages that provide scientific calculations are used NumPy The array of .
NumPy Some of the functions are as follows ：
ndarray, A fast and space-saving multidimensional array with vector arithmetic operations and complex broadcasting capabilities .
A standard mathematical function used to perform quick operations on an entire set of data （ No need to write a loop ）.
Tools for reading and writing disk data and for manipulating memory-mapped files .
linear algebra 、 Random number generation and Fourier transform function .
For integration by C、C++、Fortran Such as the language written by the code A C API.
because NumPy Provides an easy to use C API, This makes it easy to pass data to external libraries written in low-level languages , External libraries are also available NumPy The array returns the data to Python. This feature enables Python Become a kind of packaging C/C++/Fortran Selection of historical code base , And make the wrapped library have a dynamic 、 Easy-to-use interface .
NumPy By itself, it does not provide much advanced data analysis , understand NumPy Arrays and array-oriented calculations will help you use them more efficiently pandas Something like that . because NumPy It's a big problem , I'll put it in the appendix A More on NumPy Advanced features , Such as radio, .
For most data analysis applications , The features I'm most focused on are ：
For data collation and cleanup 、 Subset construction and filtering 、 Conversion and other fast vectorized array operations .
Common array algorithm , Such as sorting 、 The only change 、 Set operation, etc .
Efficient descriptive statistics and data aggregation / The operation .
For merging heterogeneous data sets / Data alignment and relational data operations for join operations .
Express conditional logic as an array expression （ Not with if-elif-else Branching cycle ）.
Data grouping operation （ polymerization 、 transformation 、 Function application, etc. ）..
