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Introduction
Matplotlib is a Python 2D plotting library which produces
publication quality figures in a variety of hardcopy formats and
interactive environments across platforms.
Matplotlib can be used
in Python scripts, the Python and IPython shell,
web application servers, and four graphical user
interface toolkits.
Matplotlib tries to make easy things easy and hard things possible.
You can generate plots, histograms, power spectra, bar charts,
errorcharts, scatterplots, etc., with just a few lines of code.
For a sampling, see the ,
For simple plotting the pyplot module provides a
MATLAB-like interface, particularly when combined
with IPython.
For the power user, you have full control
of line styles, font properties, axes properties, etc, via an object
oriented interface or via a set of functions familiar to MATLAB
Installation
Visit the .
Documentation
This is the documentation for Matplotlib version 2.0.0.
Trying to learn how to do a particular kind of plot?
Other learning resources
There are many
available including printed material, videos and tutorials.
Need help?
Matplotlib is a welcoming, inclusive project, and we try to follow
in everything we do.
Check the ,
for resources.
and the mailing lists: ,
Check out the Matplotlib questions
tool searches all of
the documentation, including full text search of over 350 complete
examples which exercise almost every corner of Matplotlib.
You can file bugs, patches and feature requests on the
but it is a good idea to ping us on the mailing list too.
To keep up to date with what's going on in Matplotlib, see
or browse the .
Anything that could require changes to your existing code
is logged in the
There are several Matplotlib add-on ,
including a choice of two projection and mapping toolkits
3d plotting with ,
axes and axis helpers in ,
several higher-level plotting interfaces
, and more.
Citing Matplotlib
Matplotlib is the brainchild of John Hunter (), who, along with its many
contributors, have put an immeasurable amount of time and effort into producing a
piece of software utilized by thousands of scientists worldwide.
If Matplotlib contributes to a project that leads to a scientific publication,
please acknowledge this work by citing the project. You can use this
Open source
through the Numfocus organization or to
The Matplotlib
is based on the Python Software Foundation
There is an active developer community and a long list of people
who have made significant .
Matplotlib is hosted on .
are tracked at Github too.Python之扩展包安装 较为方便的方法 - 简书
Python之扩展包安装 较为方便的方法
scikit learn 是Python下开源的机器学习包。（安装环境：win7.0 32bit和Python2.7）Python安装第三方扩展包较为方便的方法：easy_install + packages name在官网下载名字为
的文件。在命令行窗口运行
，安装后，可在python2.7文件夹下生成Scripts文件夹。把路径D:\Python27\Scripts添加到环境变量中，即可方便使用easy_install.exe命令了。理论上应该可以使用 easy_install命令安装matplotlib，numpy。但是由于网速限制，总是出错。所以可以选择下载.exe文件手动安装。scikit-learn需要以下包或者工具：Python (&= 2.6 or &= 3.3),NumPy (&= 1.6.1),SciPy (&= 0.9).numpy 下载链接matplotlib下载链接在安装matplotlib之前，需要安装两个辅助包，dateutil 和 pyparsing.可以采用命令 easy_install python_dateutil和easy_install pyparsing安装后手动安装matplotlib即可若运行命令import matplotlib.pyplot as plt 出现错误ImportError: No module named six； 可把路径：C:\Python27\Lib\site-packages\scipy\lib中的six.py six.pyc six.pyo三个文件拷贝到C:\Python27\Lib\site-packages目录下。安装SciPy出现如下错误：I cannot import datetime from a python script, ValueError: numpy.ufunc has the wrong size, try recompiling考虑更换版本。测试如下代码：
1 importnumpy as np
2 importmatplotlib.pyplot as plt
4 X = np.arange(-5.0, 5.0, 0.1)
5 Y = np.arange(-5.0, 5.0, 0.1)
7 x, y =np.meshgrid(X, Y)
8 f = 17 * x ** 2 - 16 * np.abs(x) * y + 17 * y ** 2 - 225
10fig =plt.figure()
11cs = plt.contour(x, y, f, 0, colors ='r')
12plt.show()
或者安装pip 命令，操作同上。下载网址https://pip.pypa.io/en/latest/installing.html
如果嫌配置麻烦，可以考虑安装IDE环境，本文推荐WinPython下载32bit或64bit的IDE. 安装后的界面和RStudio相似。
WinPython 是由 Python(x,y) 的作者开发，功能是比较全的,软件包也比较新，GUI基于PyQt， 不过相对于Python(x,y)，它主要是关注便携式安装体验：你可以把它装在u盘里面。windows中可以不再使用Python(x,y)了,改成 WinPython了。现在的版本支持Python2和3，稳定性已有相当的改善.发布的版本也比较全。winpython自带了 spyder作为Python开发环境，安装时会自动安装sypder。关于Python IDE介绍参看：Python与R的比较与R不同，Python是一门多功能的语言。数据统计是更多是通过第三方包来实现的。具体来说，我常用的Python在统计上面的Package有这样一些1.Numpy与Scipy。这两个包是Python之所以能在占有一席之地的重要原因。其中Numpy封装了基础的矩阵和向量的操作，而Scipy则在Numpy的基础上提供了更丰富的功能，比如各种统计常用的分布和算法都能迅速的在Scipy中找到。2.Matplotlib。这个Package主要是用来提供数据可视化的，其功能强大，生成的图标可以达到印刷品质，在各种学术会议里面出镜率不 低。依托于Python，可定制性相对于其他的图形库更高。还有一个优点是提供互动化的数据分析，可以动态的缩放图表，用做adhoc analysis非常合适。3.Scikit Learn。非常好用的Machine Learning库，适合于用于快速定制原型。封装几乎所有的经典算法(神经网络可能是唯一的例外，不过这个有Pylearn2来补充)，易用性极高。4.Python标准库。这里主要是体现了Python处理字符串的优势，由于Python多功能的属性和对于正则表达式的良好支持，用于处理text是在合适不过的了。基本上就日常使用就涉及这些。符号运算等等也有Sympy和Theano等强力第三方库来支持。总结，Python在你列举这些里面是综合功能最强大的，但是这些功能分散在第三方库里面，没有得到有机的整合，相应的学习成本会较高。其它意见：python与R相比速度要快。python可以直接处理上G的数据；R不行，R分析数据时需要先通过数据库把大数据转化为小数据（通过groupby）才能交给R做分析，因此R不可能直接分析行为详单，只能分析统计结果。Python=R+SQL/HiveR的优势在于有包罗万象的统计函数可以调用，特别是在时间序列分析方面（主要用在金融分析与趋势预测）无论是经典还是前沿的方法都有相应的包直接使用；相比python在这方面贫乏不少。Python的优势在于其胶水语言的特性，一些底层用C写的算法封装在python包里后性能非常高效(Python的数据挖掘包Orange canve 中的决策树分析50万用户10秒出结果，用R几个小时也出不来，8G内存全部占满)。总的来说Python是一套比较平衡的语言，各方面都可以，而R是在统计方面比较突出。但是数据分析其实不仅仅是统计，前期的数据收集，数据处理， 数据抽样，数据聚类，以及比较复杂的数据挖掘算法，数据建模等等这些任务，只要是100M以上的数据，R都很难胜任，但是Python却基本胜任。补充一下：Python有专门的数据分析包Pandas用来完成类似SQL的功能，不过Pandas是会把数据都load到内存里，如果数据太大（2G以上）需要想办法分chunk分析，或者用pytables/pyh5转换为hdf5格式的文件在硬盘上分析。另外如果是windows环境的话，建议用winpython，上面提到的这些包会自带。当然更丰富的是pythonxy可惜这个只有32位的。和SPSS是商业数据分析软件，本屌从来没用过。1.4　动手实践：在Windows上安装NumPy、Matplotlib、SciPy和IPython
本文所属图书&>&
本书是NumPy 的入门教程，主要介绍NumPy 以及相关的Python 科学计算库，如SciPy 和Matplotlib。本书内容涵盖NumPy 安装、数组对象、常用函数、矩阵运算、线性代数、金融函数、窗函数、质量控制、Matplotlib&&
在Windows上安装NumPy是必需的，但幸运的是，安装过程并不复杂，我们将在下面详细阐述。建议你安装Matplotlib、SciPy和I，虽然这一操作对于使用本书不是必需的。我们将按照如下步骤安装这些软件。
(1) 从SourceForge网站下载NumPy的Windows安装程序：
http://sourceforge.net/projects/numpy/files/
请选择合适的版本。在上图中，我们选择了numpy-1.7.0-win32-superpack-python2.7.exe。
(2) 下载完成后，双击运行安装程序。
(3) 现在，我们可以看到一段对NumPy的描述以及其特性，如上图所示。单击Next（下一步）按钮以继续安装。
(4) 如果你已经安装了，NumPy的安装程序应该能自动检测到。如果没有检测到Python，可能是你的路径设置有误。在本章的末尾，我们列出了一些在线资源，供安装NumPy时遇到问题的读者参考。
(5) 在上图中，安装程序成功检测到中已安装Python 2.7，此时应单击Next按钮继续安装；否则，请单击Cancel（取消）按钮并安装Python（NumPy不能脱离Python单独安装）。继续单击Next按钮，从这一步起就不能回退到上一步了，因此请你确认是否选择了合适的安装路径和其他安装选项。现在，真正的安装过程开始了，你需要等待一段时间。
(6) SciPy和Matplotlib可以通过Enthough安装，地址为/products/epd.php。在安装过程中，你可能需要将一个文件msp71.dll放到目录C:\Windows\system32下。你可以从这里下载这个文件：www./dllindex/dll-files.shtml?msp71。Windows下的IPython安装程序可以通过访问IPython的官网下载：
http://ipython.scipy.org/Wiki/IpythonOnWindows。
刚才做了些什么
我们在Windows上安装了NumPy、SciPy、Matplotlib以及IPython。
您对本文章有什么意见或着疑问吗？请到您的关注和建议是我们前行的参考和动力&&
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I am trying to install matplotlib on OS X Yosemite with Python 3.5. The packages which are already installed are:
appnope (0.1.0)
decorator (4.0.2)
gnureadline (6.3.3)
ipython (4.0.0)
ipython-genutils (0.1.0)
nose (1.3.7)
numpy (1.9.2)
pandas (0.16.2)
path.py (8.1.1)
pbr (1.8.0)
pexpect (3.3)
pickleshare (0.5)
pip (7.1.2)
Pygments (2.0.2)
python-dateutil (2.4.2)
pytz (2015.4)
pyzmq (14.7.0)
scipy (0.16.0)
setuptools (18.3.1)
simplegeneric (0.8.1)
six (1.9.0)
stevedore (1.8.0)
sympy (0.7.6.1)
traitlets (4.0.0)
virtualenv (13.1.2)
virtualenv-clone (0.2.6)
virtualenvwrapper (4.7.1)
wheel (0.24.0)
freetype-config --version 18.0.12
I have installed freetype via Macport
However I keep getting the following error:
Command "python setup.py egg_info" failed with error code 1 in /private/var/folders/_t/n0g5nlhdlnf127h0000gn/T/pip-build-82786k0f/matplotlib
The complete message I receive after the installation command is:
pip3 install matplotlib
Collecting matplotlib
Using cached matplotlib-1.4.3.tar.gz
Complete output from command python setup.py egg_info:
IMPORTANT WARNING:
pkg-config is not installed.
matplotlib may not be able to find some of its dependencies
============================================================================
Edit setup.cfg to change the build options
BUILDING MATPLOTLIB
matplotlib: yes [1.4.3]
python: yes [3.5.0 (v3.5.0:374f501f4567, Sep 12 2015,
[GCC 4.2.1 (Apple Inc. build 5666) (dot
platform: yes [darwin]
REQUIRED DEPENDENCIES AND EXTENSIONS
numpy: yes [version 1.9.2]
six: yes [using six version 1.9.0]
dateutil: yes [using dateutil version 2.4.2]
pytz: yes [using pytz version 2015.4]
tornado: yes [tornado was not found. It is required for the
WebAgg backend. pip/easy_install may attempt to
install it after matplotlib.]
pyparsing: yes [pyparsing was not found. It is required for
mathtext support. pip/easy_install may attempt to
install it after matplotlib.]
pycxx: yes [Official versions of PyCXX are not compatible
with matplotlib on Python 3.x, since they lack
support for the buffer object.
Using local copy]
libagg: yes [pkg-config information for 'libagg' could not
be found. Using local copy.]
freetype: no
[The C/C++ header for freetype2 (ft2build.h)
could not be found.
You may need to install the
development package.]
png: yes [version 1.6.18]
qhull: yes [pkg-config information for 'qhull' could not be
found. Using local copy.]
OPTIONAL SUBPACKAGES
sample_data: yes [installing]
toolkits: yes [installing]
tests: yes [using nose version 1.3.7 / using unittest.mock]
toolkits_tests: yes [using nose version 1.3.7 / using unittest.mock]
OPTIONAL BACKEND EXTENSIONS
macosx: yes [installing, darwin]
qt5agg: no
[PyQt5 not found]
qt4agg: no
[PyQt4 not found]
pyside: no
[PySide not found]
gtk3agg: no
[Requires pygobject to be installed.]
gtk3cairo: no
[Requires cairocffi or pycairo to be installed.]
gtkagg: no
[Requires pygtk]
tkagg: yes [installing, version version not identified]
[requires wxPython]
[Requires pygtk]
agg: yes [installing]
[cairocffi or pycairo not found]
windowing: no
[Microsoft Windows only]
OPTIONAL LATEX DEPENDENCIES
dvipng: yes [version 1.15]
ghostscript: yes [version 9.16]
latex: yes [version 3.]
pdftops: no
============================================================================
* The following required packages can not be built:
* freetype
----------------------------------------
17.1k44671
it seems you haven't pkg-config installed.
Just install it via brew:
brew install pkg-config
To be on the safer side also install the unresolved dependencies:
pip3 install tornado pyparsing
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