Ipython
提供了一个很好的解释器界面。
Matplotlib
提供了一个类似 Matlab
的画图工具。
Numpy
提供了 ndarray
对象,可以进行快速的向量化计算。
Scipy
是 Python
中进行科学计算的一个第三方库,以 Numpy
为基础。
Pandas
是处理时间序列数据的第三方库,提供一个类似 R
语言的环境。
StatsModels
是一个统计库,着重于统计模型。
Scikits
以 Scipy
为基础,提供如 scikits-learn
机器学习和scikits-image
图像处理等高级用法。
Scipy
由不同科学计算领域的子模块组成:
子模块 | 描述 |
---|---|
cluster |
聚类算法 |
constants |
物理数学常数 |
fftpack |
快速傅里叶变换 |
integrate |
积分和常微分方程求解 |
interpolate |
插值 |
io |
输入输出 |
linalg |
线性代数 |
odr |
正交距离回归 |
optimize |
优化和求根 |
signal |
信号处理 |
sparse |
稀疏矩阵 |
spatial |
空间数据结构和算法 |
special |
特殊方程 |
stats |
统计分布和函数 |
weave |
C/C++ 积分 |
在使用 Scipy
之前,为了方便,假定这些基础的模块已经被导入:
import numpy as np
import scipy as sp
import matplotlib as mpl
import matplotlib.pyplot as plt
使用 Scipy 中的子模块时,需要分别导入:
from scipy import linalg, optimize
对于一些常用的函数,这些在子模块中的函数可以在 scipy
命名空间中调用。另一方面,由于 Scipy
以 Numpy
为基础,因此很多基础的 Numpy
函数可以在scipy
命名空间中直接调用。
我们可以使用 numpy
中的 info
函数来查看函数的文档:
np.info(optimize.fmin)
fmin(func, x0, args=(), xtol=0.0001, ftol=0.0001, maxiter=None, maxfun=None, full_output=0, disp=1, retall=0, callback=None) Minimize a function using the downhill simplex algorithm. This algorithm only uses function values, not derivatives or second derivatives. Parameters ---------- func : callable func(x,*args) The objective function to be minimized. x0 : ndarray Initial guess. args : tuple, optional Extra arguments passed to func, i.e. ``f(x,*args)``. callback : callable, optional Called after each iteration, as callback(xk), where xk is the current parameter vector. xtol : float, optional Relative error in xopt acceptable for convergence. ftol : number, optional Relative error in func(xopt) acceptable for convergence. maxiter : int, optional Maximum number of iterations to perform. maxfun : number, optional Maximum number of function evaluations to make. full_output : bool, optional Set to True if fopt and warnflag outputs are desired. disp : bool, optional Set to True to print convergence messages. retall : bool, optional Set to True to return list of solutions at each iteration. Returns ------- xopt : ndarray Parameter that minimizes function. fopt : float Value of function at minimum: ``fopt = func(xopt)``. iter : int Number of iterations performed. funcalls : int Number of function calls made. warnflag : int 1 : Maximum number of function evaluations made. 2 : Maximum number of iterations reached. allvecs : list Solution at each iteration. See also -------- minimize: Interface to minimization algorithms for multivariate functions. See the 'Nelder-Mead' `method` in particular. Notes ----- Uses a Nelder-Mead simplex algorithm to find the minimum of function of one or more variables. This algorithm has a long history of successful use in applications. But it will usually be slower than an algorithm that uses first or second derivative information. In practice it can have poor performance in high-dimensional problems and is not robust to minimizing complicated functions. Additionally, there currently is no complete theory describing when the algorithm will successfully converge to the minimum, or how fast it will if it does. References ---------- .. [1] Nelder, J.A. and Mead, R. (1965), "A simplex method for function minimization", The Computer Journal, 7, pp. 308-313 .. [2] Wright, M.H. (1996), "Direct Search Methods: Once Scorned, Now Respectable", in Numerical Analysis 1995, Proceedings of the 1995 Dundee Biennial Conference in Numerical Analysis, D.F. Griffiths and G.A. Watson (Eds.), Addison Wesley Longman, Harlow, UK, pp. 191-208.
可以用 lookfor
来查询特定关键词相关的函数:
np.lookfor("resize array")
Search results for 'resize array' --------------------------------- numpy.chararray.resize Change shape and size of array in-place. numpy.ma.resize Return a new masked array with the specified size and shape. numpy.oldnumeric.ma.resize The original array's total size can be any size. numpy.resize Return a new array with the specified shape. numpy.chararray chararray(shape, itemsize=1, unicode=False, buffer=None, offset=0, numpy.memmap Create a memory-map to an array stored in a *binary* file on disk. numpy.ma.mvoid.resize .. warning::
还可以指定查找的模块:
np.lookfor("remove path", module="os")
Search results for 'remove path' -------------------------------- os.removedirs removedirs(path) os.walk Directory tree generator.