1 安装配置开发环境
1.1 准备安装
下载以下软件
Eclipse for C/C++
SUN JDK 1.6 不一定要1.6,1.5的也可以
Python3.1 Python2.6.4 Pythone2.5.2
mod_python-3.3.1.win32-py2.5-Apache2.2
MySQL-python-1.2.2.win32-py2.5
pysqlite-2.5.5.win32-py2.5
python.pydev.feature-1.5.0.1251989166-sources.zip
Django-1.1.1.tar.gz
1.2 Apache安装配置
懒得配置了,直接从网上找了一个Apache+mysql+php的套件,
可以去http://www.newhua.com/soft/71111.htm下载,再安装mod_python-3.3.1.win32-py2.5-Apache2.2.exe让apache支持python现在我们来配置C:\ ESSamp\Apache2\conf\httpd.conf文件,加入
LoadModule python_module modules/mod_python.so
运行ESSamp,点击“运行”启动服务
点击“浏览默认网站”,看到以下画面,即安装成功
1.1 Eclipse、python安装配置
1.1.1 安装 JDK
1.1.2 解压Eclipse,我是放在C:\eclipse下
1.1.3 安装python
安装相应Python版本,这里用的是python2.5版,现在Django只支持到2.5,其它各版本可以同时存在,安装MySQL-python-1.2.2.win32-py2.5 让python支持mysql数据库,pysqlite-2.5.5.win32-py2.5让python支持sqlite数据库,解压python.pydev.feature-1.5.0.1251989166-sources.zip,将里面的两个目录拷贝到eclipse安装的根目录下“C:\eclipse”
1.1.4 配置环境变量,在桌面“我的电脑”点击“属性”菜单,选择“高级”选项卡,点击“环境变量…”按钮,修改系统变量 Path=;%JAVA_HOME%\bin,直接加在后面,如图
再新增两个系统变量
JAVA_HOME=C:\Program Files\Java\jdk1.6.0
CLASSPATH=.;%JAVA_HOME%\lib\tools.jar;%JAVA_HOME%\lib\dt.jar
1.1.5 启动Eclipse,选择菜单“Help”->“Install New Software…”,弹出对话框,如图
点击“Add…”按钮,输入地址,点击“OK”按钮,如图
在“work with”选择刚新添加的Pydev Extensions,会看到最新的软件版本
全选后点击“Next>”
全选后点击“Finish”完成升级。
1.1.6 选择菜单“Window”->“Preferences”,弹出对话框,如图
点击“New…”按钮装python安装路径添加进来,点击“OK”。
1.2 Django安装配置
1.2.1 安装
解压Django,我是解压到python25下,在Django目录中有个setyp.py文件,打开运行窗口进行安装
1.2.2 为了方便开发,我们再来进行配置系统变量
path= C:\Python25\Scripts;C:\Python25\Lib\site-packages\django\bin
1.2.3 测试Django是否安装成功
首先看看C:\Python25\Lib\site-packages\django\bin是否存在,然后创建一个工程
Django-admin.py startproject testemo
运行manage.py runserver 后,在浏览器地址栏中输入http://127.0.0.1:8000/
出现以上画面,即安装成功

The reasons why Python scripts cannot run on Unix systems include: 1) Insufficient permissions, using chmod xyour_script.py to grant execution permissions; 2) Shebang line is incorrect or missing, you should use #!/usr/bin/envpython; 3) The environment variables are not set properly, and you can print os.environ debugging; 4) Using the wrong Python version, you can specify the version on the Shebang line or the command line; 5) Dependency problems, using virtual environment to isolate dependencies; 6) Syntax errors, using python-mpy_compileyour_script.py to detect.

Using Python arrays is more suitable for processing large amounts of numerical data than lists. 1) Arrays save more memory, 2) Arrays are faster to operate by numerical values, 3) Arrays force type consistency, 4) Arrays are compatible with C arrays, but are not as flexible and convenient as lists.

Listsare Better ForeflexibilityandMixdatatatypes, Whilearraysares Superior Sumerical Computation Sand Larged Datasets.1) Unselable List Xibility, MixedDatatypes, andfrequent elementchanges.2) Usarray's sensory -sensical operations, Largedatasets, AndwhenMemoryEfficiency

NumPymanagesmemoryforlargearraysefficientlyusingviews,copies,andmemory-mappedfiles.1)Viewsallowslicingwithoutcopying,directlymodifyingtheoriginalarray.2)Copiescanbecreatedwiththecopy()methodforpreservingdata.3)Memory-mappedfileshandlemassivedatasetsb

ListsinPythondonotrequireimportingamodule,whilearraysfromthearraymoduledoneedanimport.1)Listsarebuilt-in,versatile,andcanholdmixeddatatypes.2)Arraysaremorememory-efficientfornumericdatabutlessflexible,requiringallelementstobeofthesametype.

Pythonlistscanstoreanydatatype,arraymodulearraysstoreonetype,andNumPyarraysarefornumericalcomputations.1)Listsareversatilebutlessmemory-efficient.2)Arraymodulearraysarememory-efficientforhomogeneousdata.3)NumPyarraysareoptimizedforperformanceinscient

WhenyouattempttostoreavalueofthewrongdatatypeinaPythonarray,you'llencounteraTypeError.Thisisduetothearraymodule'sstricttypeenforcement,whichrequiresallelementstobeofthesametypeasspecifiedbythetypecode.Forperformancereasons,arraysaremoreefficientthanl

Pythonlistsarepartofthestandardlibrary,whilearraysarenot.Listsarebuilt-in,versatile,andusedforstoringcollections,whereasarraysareprovidedbythearraymoduleandlesscommonlyusedduetolimitedfunctionality.


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