1.什么是numpy
NumPy系统是Python的一种开源的数值计算扩展。这种工具可用来存储和处理大型矩阵，比Python自身的嵌套列表（nested list structure)结构要高效的多（该结构也可以用来表示矩阵（matrix））。
包括：
1、一个强大的N维数组对象Array；
2、比较成熟的（广播）函数库；
3、用于整合C/C++和Fortran代码的工具包；
4、实用的线性代数、傅里叶变换和随机数生成函数。
numpy和稀疏矩阵运算包scipy配合使用更加方便。
2.搭建numpy环境
在安装python的环境下，用pip管理工具安装（没有安装pip应先安装pip）：
安装pip：sudo apt-get install pip
安装numpy：sudo pip install numpy
安装scipy：sudo pip install scipy
安装matplotlib：sudo pip install matplotlib
3.如何学习
进入python安装包目录
查看安装的numpy包下的__ini__.py文件
"""
NumPy
=====
Provides
1. An array object of arbitrary homogeneous items
2. Fast mathematical operations over arrays
3. Linear Algebra, Fourier Transforms, Random Number Generation
How to use the documentation
----------------------------
Documentation is available in two forms: docstrings provided
with the code, and a loose standing reference guide, available from
`the NumPy homepage <http://www.scipy.org>`_.
We recommend exploring the docstrings using
`IPython <http://ipython.scipy.org>`_, an advanced Python shell with
TAB-completion and introspection capabilities. See below for further
instructions.
The docstring examples assume that `numpy` has been imported as `np`::
>>> import numpy as np
Code snippets are indicated by three greater-than signs::
>>> x = 42
>>> x = x + 1
Use the built-in ``help`` function to view a function's docstring::
>>> help(np.sort)
... # doctest: +SKIP
For some objects, ``np.info(obj)`` may provide additional help. This is
particularly true if you see the line "Help on ufunc object:" at the top
of the help() page. Ufuncs are implemented in C, not Python, for speed.
The native Python help() does not know how to view their help, but our
np.info() function does.
To search for documents containing a keyword, do::
>>> np.lookfor('keyword')
... # doctest: +SKIP
General-purpose documents like a glossary and help on the basic concepts
of numpy are available under the ``doc`` sub-module::
>>> from numpy import doc
>>> help(doc)
... # doctest: +SKIP
Available subpackages
---------------------
doc
Topical documentation on broadcasting, indexing, etc.
lib
Basic functions used by several sub-packages.
random
Core Random Tools
linalg
Core Linear Algebra Tools
fft
Core FFT routines
polynomial
Polynomial tools
testing
NumPy testing tools
f2py
Fortran to Python Interface Generator.
distutils
Enhancements to distutils with support for
Fortran compilers support and more.
Utilities
---------
test
Run numpy unittests
show_config
Show numpy build configuration
dual
Overwrite certain functions with high-performance Scipy tools
matlib
Make everything matrices.
__version__
NumPy version string
Viewing documentation using IPython
-----------------------------------
Start IPython with the NumPy profile (``ipython -p numpy``), which will
import `numpy` under the alias `np`. Then, use the ``cpaste`` command to
paste examples into the shell. To see which functions are available in
`numpy`, type ``np.<TAB>`` (where ``<TAB>`` refers to the TAB key), or use
``np.*cos*?<ENTER>`` (where ``<ENTER>`` refers to the ENTER key) to narrow
down the list. To view the docstring for a function, use
``np.cos?<ENTER>`` (to view the docstring) and ``np.cos??<ENTER>`` (to view
the source code).
Copies vs. in-place operation
-----------------------------
Most of the functions in `numpy` return a copy of the array argument
(e.g., `np.sort`). In-place versions of these functions are often
available as array methods, i.e. ``x = np.array([1,2,3]); x.sort()``.
Exceptions to this rule are documented.
"""
from __future__ import division, absolute_import, print_function
import sys
import warnings
from ._globals import ModuleDeprecationWarning, VisibleDeprecationWarning
from ._globals import _NoValue
# We first need to detect if we're being called as part of the numpy setup
# procedure itself in a reliable manner.
try:
__NUMPY_SETUP__
except NameError:
__NUMPY_SETUP__ = False
if __NUMPY_SETUP__:
sys.stderr.write('Running from numpy source directory.
')
else:
try:
from numpy.__config__ import show as show_config
except ImportError:
msg = """Error importing numpy: you should not try to import numpy from
its source directory; please exit the numpy source tree, and relaunch
your python interpreter from there."""
raise ImportError(msg)
from .version import git_revision as __git_revision__
from .version import version as __version__
from ._import_tools import PackageLoader
def pkgload(*packages, **options):
loader = PackageLoader(infunc=True)
return loader(*packages, **options)
from . import add_newdocs
__all__ = ['add_newdocs',
'ModuleDeprecationWarning',
'VisibleDeprecationWarning']
pkgload.__doc__ = PackageLoader.__call__.__doc__
# We don't actually use this ourselves anymore, but I'm not 100% sure that
# no-one else in the world is using it (though I hope not)
from .testing import Tester
test = testing.nosetester._numpy_tester().test
bench = testing.nosetester._numpy_tester().bench
# Allow distributors to run custom init code
from . import _distributor_init
from . import core
from .core import *
from . import compat
from . import lib
from .lib import *
from . import linalg
from . import fft
from . import polynomial
from . import random
from . import ctypeslib
from . import ma
from . import matrixlib as _mat
from .matrixlib import *
from .compat import long
# Make these accessible from numpy name-space
# but not imported in from numpy import *
if sys.version_info[0] >= 3:
from builtins import bool, int, float, complex, object, str
unicode = str
else:
from __builtin__ import bool, int, float, complex, object, unicode, str
from .core import round, abs, max, min
__all__.extend(['__version__', 'pkgload', 'PackageLoader',
'show_config'])
__all__.extend(core.__all__)
__all__.extend(_mat.__all__)
__all__.extend(lib.__all__)
__all__.extend(['linalg', 'fft', 'random', 'ctypeslib', 'ma'])
# Filter annoying Cython warnings that serve no good purpose.
warnings.filterwarnings("ignore", message="numpy.dtype size changed")
warnings.filterwarnings("ignore", message="numpy.ufunc size changed")
warnings.filterwarnings("ignore", message="numpy.ndarray size changed")
# oldnumeric and numarray were removed in 1.9. In case some packages import
# but do not use them, we define them here for backward compatibility.
oldnumeric = 'removed'
numarray = 'removed'
此处告诉我们numpy提供什么功能支持，如何使用文档，如何使用numpy内置的帮助功能，可用的子包等等信息。
现在就开始学习！