01.Install and basesyntax.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Python安装"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "具体安装可以参见这篇文章: [python3环境搭建](http://www.runoob.com/python3/python3-install.html)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b><font color='red'>请尽量安装3.6即可，现在大部分Python包，都支持3.7了，包括TensorFlow。可以这样说，TensorFlow能支持什么版本的Python，就安装什么版本的Python。</font></b>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 安装注意事项"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 安装32位还是64位版本？"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这样说吧，都9102年了，就别安装32位的了。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 安装地址：[python-368 Windows x86-64 executable installer](https://www.python.org/ftp/python/3.6.8/python-3.6.8-amd64.exe)\n",
    "<img src='./image/install.png' />\n",
    "\n",
    "- 举个例子：Tensorflow必须64位的python，目前支持python版本，只有64位（毕竟是张量运算，需要内存支持）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Windows环境需要Visual C++ SDK"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们在Windows环境安装python组件包时，最常见的错误是：<b>ERROR MICROSOFT VISUAL C++ 14.0 IS REQUIRED</b>\n",
    "\n",
    "原因：Windows下面的Python需要Visual C++库的SDK作为编译工具，例如：\n",
    "\n",
    "setuptools.extension.Extension或者numpy.distutils.core.Extension。\n",
    "\n",
    "而在Linux或Max系统，编译器已经集成了C++库。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>解决方案</b>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "参见：[FIX PYTHON 3 ON WINDOWS ERROR MICROSOFT VISUAL C++ 14.0 IS REQUIRED](https://www.scivision.co/python-windows-visual-c++-14-required/), 这篇文章详细描述了解决方案。\n",
    "\n",
    "可以从如下链接，下载[Build Tools for Visual Studio 2017](https://visualstudio.microsoft.com/thank-you-downloading-visual-studio/?sku=BuildTools&rel=15)\n",
    "\n",
    "- 因为需要6G磁盘空间，建议选择非系统盘，作为安装路径。但是强制安装在C盘的文件大小也有几个G\n",
    "- 安装选项，选择Visual C++ Build Tools\n",
    "- 最好勾选Windows 10 SDK"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 提前下载各种需要的python安装文件"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "whl格式本质上是一个压缩包，里面包含了py文件，以及经过编译的pyd文件。使得可以在不具备编译环境的情况下，选择合适自己的python环境进行安装。\n",
    "\n",
    "安装方法很简单，进入命令行输入：pip install xxxx.whl\n",
    "\n",
    "或者如果是升级：pip install -U xxxx.whl"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "著名的非官方适用Windows环境的whl的下载地址：[Unofficial Windows Binaries for Python Extension Packages](https://www.lfd.uci.edu/~gohlke/pythonlibs/)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 下载哪个安装文件？"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "很多刚入坑的同学，看到那么多安装文件列表，可能会蒙圈，如何选择是一个问题。\n",
    "如:<img src='./image/numpyinstall.png' />"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可能会产生，我是谁，我在哪，我要干什么的错觉，其实选择安装文件很简单：\n",
    "- 确定python版本，如3.6x，那么就筛选含有<b>cp36m</b>的下载列表；\n",
    "- 确定python运行环境，如果是64位，那么就选择含有<b>amd64</b>的下载列表\n",
    "- 确定安装包版本，Numpy目前1.15.4是稳定版本，那么就选择含有<b>1.15.4</b>的下载列表\n",
    "- 经过这样的一番选择，就得到唯一的一个下载选项：numpy‑1.15.4+mkl‑cp36‑cp36m‑win_amd64.whl"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 无法import sklearn (Scikit-Learn无法加载)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 原因：Scikit-Learn需要Numpy+MKL才能加载，纯Numpy无法加载Scikit-Learn\n",
    "- 解决方案：下载包含MKL的Numpy版本：[numpy‑1.15.4+mkl‑cp36‑cp36m‑win_amd64.whl](https://www.lfd.uci.edu/~gohlke/pythonlibs/#numpy)\n",
    "- 安装whl文件：进入到whl的下载路径，直接pip安装，如：\n",
    "\n",
    "```\n",
    "pip install numpy-1.15.2+mkl-cp36-cp36m-win_amd64.whl\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Python运行时，遇到No Module xxx该怎么办？"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "缺什么补什么，即缺少什么模块就通过pip安装就好了。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "比如Pandas：\n",
    "- 读取Excel需要xlrd模块，那么就通过```pip install xlrd```进行安装\n",
    "- 写入Excel需要openpyxl模型，那么就通过```pip install openpyxl```进行安装\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 我提供的一个安装超超多包的python脚本"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "地址位于课件根目录的: install_need_package.py\n",
    "\n",
    "对应的安装包目录位于根目录的: all_requirements.txt\n",
    "\n",
    "因为我是通过```pip freeze > all_requirements.txt```生成的组件包列表文件，可以说应有尽有了。。。\n",
    "\n",
    "**强调一遍：如果是Windows系统，一定先安装Visual Studio 2015或者Visual Studio 2017啊**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "安装包目录如下：\n",
    "```\n",
    "absl-py==0.4.1\n",
    "alabaster==0.7.12\n",
    "allennlp==0.8.0\n",
    "amqp==2.3.2\n",
    "appdirs==1.4.3\n",
    "asn1crypto==0.24.0\n",
    "astor==0.7.1\n",
    "astroid==2.0.4\n",
    "atomicwrites==1.2.1\n",
    "attrs==18.2.0\n",
    "Automat==0.7.0\n",
    "aws-xray-sdk==0.95\n",
    "awscli==1.16.81\n",
    "Babel==2.6.0\n",
    "backcall==0.1.0\n",
    "beautifulsoup4==4.6.3\n",
    "billiard==3.5.0.5\n",
    "bleach==2.1.4\n",
    "bokeh==1.0.3\n",
    "boto==2.49.0\n",
    "boto3==1.9.4\n",
    "botocore==1.12.4\n",
    "bs4==0.0.1\n",
    "business-calendar==0.2.1\n",
    "bz2file==0.98\n",
    "cached-property==1.5.1\n",
    "cachetools==2.1.0\n",
    "camelot-py==0.7.1\n",
    "celery==4.2.1\n",
    "certifi==2018.8.24\n",
    "cffi==1.11.5\n",
    "chardet==3.0.4\n",
    "chartify==2.3.5\n",
    "cld2-cffi==0.1.4\n",
    "Click==7.0\n",
    "client==0.0.1\n",
    "cloudpickle==0.7.0\n",
    "colorama==0.3.9\n",
    "colour==0.1.5\n",
    "configparser==3.5.0\n",
    "conllu==0.11\n",
    "constantly==15.1.0\n",
    "cookies==2.2.1\n",
    "cryptography==2.3.1\n",
    "cssselect==1.0.3\n",
    "cycler==0.10.0\n",
    "cymem==1.31.2\n",
    "cypari==2.3.0\n",
    "Cython==0.28.5\n",
    "cytoolz==0.9.0.1\n",
    "dask==1.1.0\n",
    "date-extractor==3.9.1\n",
    "datefinder==0.6.1\n",
    "decorator==4.3.0\n",
    "defusedxml==0.5.0\n",
    "Deprecated==1.2.4\n",
    "dill==0.2.8.2\n",
    "docker==3.6.0\n",
    "docker-pycreds==0.4.0\n",
    "docutils==0.14\n",
    "dopamine-rl==2.0.1\n",
    "dukpy==0.2.2\n",
    "ecdsa==0.13\n",
    "editdistance==0.5.2\n",
    "en-core-web-lg==2.0.0\n",
    "en-core-web-sm==2.0.0\n",
    "entrypoints==0.2.3\n",
    "enum34==1.1.6\n",
    "ephem==3.7.6.0\n",
    "et-xmlfile==1.0.1\n",
    "excalibur-py==0.4.1\n",
    "Faker==1.0.1\n",
    "fasttext==0.8.3\n",
    "flair==0.4.0\n",
    "flaky==3.4.0\n",
    "flasgger==0.9.1\n",
    "Flask==1.0.2\n",
    "Flask-Cors==3.0.7\n",
    "ftfy==4.4.3\n",
    "funcy==1.11\n",
    "future==0.16.0\n",
    "fuzzywuzzy==0.17.0\n",
    "FXrays==1.3.3\n",
    "gast==0.2.0\n",
    "gensim==3.4.0\n",
    "geos==0.2.1\n",
    "gevent==1.3.6\n",
    "gin-config==0.1.4\n",
    "google-api-python-client==1.7.8\n",
    "google-auth==1.6.3\n",
    "google-auth-httplib2==0.0.3\n",
    "googleapis-common-protos==1.5.8\n",
    "graphviz==0.10.1\n",
    "greenlet==0.4.15\n",
    "grpcio==1.15.0\n",
    "gunicorn==19.9.0\n",
    "gym==0.11.0\n",
    "h5py==2.8.0\n",
    "hdbscan==0.8.18\n",
    "html5lib==1.0.1\n",
    "HTMLParser==0.0.2\n",
    "httplib2==0.12.1\n",
    "hyperlink==18.0.0\n",
    "hyperopt==0.1.1\n",
    "hypothesis==2.0.0\n",
    "idna==2.7\n",
    "ijson==2.3\n",
    "imageio==2.5.0\n",
    "imagesize==1.1.0\n",
    "imbalanced-learn==0.3.3\n",
    "imblearn==0.0\n",
    "imgaug==0.2.8\n",
    "incremental==17.5.0\n",
    "ipykernel==4.9.0\n",
    "ipython==6.5.0\n",
    "ipython-genutils==0.2.0\n",
    "ipywidgets==7.4.2\n",
    "isodate==0.6.0\n",
    "isort==4.3.4\n",
    "itsdangerous==0.24\n",
    "javascripthon==0.10\n",
    "jdcal==1.4\n",
    "jedi==0.12.1\n",
    "jieba==0.39\n",
    "Jinja2==2.10\n",
    "jmespath==0.9.3\n",
    "joblib==0.13.0\n",
    "jsondiff==1.1.1\n",
    "jsonpickle==1.0\n",
    "jsonschema==2.6.0\n",
    "jupyter==1.0.0\n",
    "jupyter-client==5.2.3\n",
    "jupyter-console==5.2.0\n",
    "jupyter-contrib-core==0.3.3\n",
    "jupyter-contrib-nbextensions==0.5.0\n",
    "jupyter-core==4.4.0\n",
    "jupyter-echarts-pypkg==0.1.2\n",
    "jupyter-highlight-selected-word==0.2.0\n",
    "jupyter-latex-envs==1.4.6\n",
    "jupyter-nbextensions-configurator==0.4.1\n",
    "jupyter-pip==0.3.1\n",
    "jupyterthemes==0.20.0\n",
    "Keras==2.2.4\n",
    "Keras-Applications==1.0.6\n",
    "keras-bert==0.24.0\n",
    "keras-layer-normalization==0.2.0\n",
    "keras-multi-head==0.14.0\n",
    "keras-pos-embd==0.6.0\n",
    "keras-position-wise-feed-forward==0.1.0\n",
    "Keras-Preprocessing==1.0.5\n",
    "keras-self-attention==0.31.0\n",
    "keras-transformer==0.11.0\n",
    "keyring==18.0.0\n",
    "kiwisolver==1.0.1\n",
    "kombu==4.2.2.post1\n",
    "lazy-object-proxy==1.3.1\n",
    "lda==1.1.0\n",
    "lesscpy==0.13.0\n",
    "livereload==2.6.0\n",
    "lml==0.0.2\n",
    "ludwig==0.1.0\n",
    "lunardate==0.1.5\n",
    "lxml==4.2.5\n",
    "macropy3==1.1.0b2\n",
    "Markdown==2.6.11\n",
    "MarkupSafe==1.0\n",
    "matplotlib==3.0.0\n",
    "mccabe==0.6.1\n",
    "mesh-tensorflow==0.0.5\n",
    "mistune==0.8.3\n",
    "mkdocs==1.0.4\n",
    "mock==2.0.0\n",
    "more-itertools==4.3.0\n",
    "moto==1.3.4\n",
    "mpl-finance==0.10.0\n",
    "mpld3==0.3\n",
    "mpmath==1.1.0\n",
    "msgpack==0.5.6\n",
    "msgpack-numpy==0.4.1\n",
    "msgpack-python==0.5.4\n",
    "murmurhash==0.28.0\n",
    "nbconvert==5.4.0\n",
    "nbformat==4.4.0\n",
    "networkx==2.1\n",
    "nltk==3.3\n",
    "notebook==5.6.0\n",
    "notedown==1.5.1\n",
    "numexpr==2.6.9\n",
    "numpydoc==0.8.0\n",
    "oauth2client==4.1.3\n",
    "opencc-python-reimplemented==0.1.4\n",
    "opencv-python==4.0.1\n",
    "openpyxl==2.5.9\n",
    "overrides==1.9\n",
    "packaging==18.0\n",
    "pandas==0.23.4\n",
    "pandoc-attributes==0.1.7\n",
    "pandocfilters==1.4.2\n",
    "parsel==1.5.0\n",
    "parsimonious==0.8.0\n",
    "parso==0.3.1\n",
    "pathlib==1.0.1\n",
    "pbr==3.1.1\n",
    "pdf2image==1.4.2\n",
    "pdfminer.six==20181108\n",
    "pdfminer3k==1.3.1\n",
    "pickleshare==0.7.4\n",
    "Pillow==5.2.0\n",
    "pkuseg==0.0.11\n",
    "plac==0.9.6\n",
    "plink==2.2\n",
    "pluggy==0.7.1\n",
    "ply==3.11\n",
    "preshed==1.0.1\n",
    "prometheus-client==0.3.1\n",
    "promise==2.2.1\n",
    "prompt-toolkit==1.0.15\n",
    "protobuf==3.6.1\n",
    "py==1.6.0\n",
    "pyaml==18.11.0\n",
    "pyasn1==0.4.4\n",
    "pyasn1-modules==0.2.2\n",
    "pyCalverter==1.6.1\n",
    "pycparser==2.18\n",
    "pycryptodome==3.6.6\n",
    "PyDispatcher==2.0.5\n",
    "pydot==1.4.1\n",
    "pyecharts==0.2.0\n",
    "pyecharts-javascripthon==0.0.6\n",
    "pyecharts-jupyter-installer==0.0.3\n",
    "pyemd==0.5.1\n",
    "pyglet==1.3.2\n",
    "Pygments==2.2.0\n",
    "PyHamcrest==1.9.0\n",
    "pyLDAvis==2.1.2\n",
    "pylint==2.1.1\n",
    "pymongo==3.7.2\n",
    "pymssql==2.1.3\n",
    "PyMuPDF==1.13.20\n",
    "pyOpenSSL==18.0.0\n",
    "pyparsing==2.2.0\n",
    "PyPDF2==1.26.0\n",
    "Pyphen==0.9.5\n",
    "pypiwin32==223\n",
    "pypng==0.0.18\n",
    "pyquery==1.4.0\n",
    "pyreadline==2.1\n",
    "pytest==3.8.0\n",
    "python-dateutil==2.7.3\n",
    "python-jose==2.0.2\n",
    "python-Levenshtein==0.12.0\n",
    "PythonMagick==0.9.19\n",
    "pytorch-pretrained-bert==0.3.0\n",
    "pytz==2017.3\n",
    "PyWavelets==1.0.1\n",
    "pywin32==224\n",
    "pywin32-ctypes==0.2.0\n",
    "pywinpty==0.5.4\n",
    "PyYAML==3.13\n",
    "pyzmq==17.1.2\n",
    "qtconsole==4.4.1\n",
    "QtPy==1.5.0\n",
    "queuelib==1.5.0\n",
    "regex==2017.4.5\n",
    "requests==2.19.1\n",
    "requests-toolbelt==0.9.1\n",
    "responses==0.10.5\n",
    "rsa==3.4.2\n",
    "s3transfer==0.1.13\n",
    "scikit-image==0.14.2\n",
    "scikit-learn==0.20.2\n",
    "scikit-surprise==1.0.3\n",
    "scipy==1.0.0\n",
    "Scrapy==1.4.0\n",
    "seaborn==0.9.0\n",
    "segtok==1.5.7\n",
    "selenium==3.8.0\n",
    "Send2Trash==1.5.0\n",
    "service-identity==17.0.0\n",
    "shap==0.27.2\n",
    "Shapely==1.6.4.post1\n",
    "simplegeneric==0.8.1\n",
    "simplejson==3.16.0\n",
    "six==1.11.0\n",
    "sklearn==0.0\n",
    "smart-open==1.6.0\n",
    "snappy-manifolds==1.0\n",
    "snowballstemmer==1.2.1\n",
    "sortedcontainers==2.1.0\n",
    "spacy==2.0.7\n",
    "spherogram==1.8.1\n",
    "Sphinx==1.8.3\n",
    "sphinxcontrib-websupport==1.1.0\n",
    "SQLAlchemy==1.2.15\n",
    "sqlitedict==1.6.0\n",
    "sqlparse==0.2.4\n",
    "stop-words==2018.7.23\n",
    "suds==0.4\n",
    "sympy==1.3\n",
    "TA-Lib==0.4.17\n",
    "tabulate==0.8.3\n",
    "tensor2tensor==1.12.0\n",
    "tensorboard==1.12.2\n",
    "tensorboardX==1.2\n",
    "tensorflow==1.12.0\n",
    "tensorflow-metadata==0.9.0\n",
    "tensorflow-probability==0.5.0\n",
    "termcolor==1.1.0\n",
    "terminado==0.8.1\n",
    "testpath==0.3.1\n",
    "text-unidecode==1.2\n",
    "textacy==0.6.2\n",
    "textrank4zh==0.3\n",
    "tf-agents==0.2.0rc2\n",
    "tfds-nightly==1.0.1.dev201902190105\n",
    "tflearn==0.3.2\n",
    "thinc==6.10.2\n",
    "toolz==0.9.0\n",
    "torch==1.0.0\n",
    "torchvision==0.2.1\n",
    "tornado==5.1\n",
    "tqdm==4.26.0\n",
    "traitlets==4.3.2\n",
    "tuchart==0.1.2.7\n",
    "tushare==0.8.6\n",
    "Twisted==17.9.0\n",
    "typed-ast==1.1.0\n",
    "typing==3.6.4\n",
    "ujson==1.35\n",
    "Unidecode==1.0.22\n",
    "uritemplate==3.0.0\n",
    "urllib3==1.23\n",
    "vine==1.2.0\n",
    "virtualenv==16.0.0\n",
    "w3lib==1.19.0\n",
    "Wand==0.4.5\n",
    "wcwidth==0.1.7\n",
    "webencodings==0.5.1\n",
    "websocket-client==0.54.0\n",
    "Werkzeug==0.14.1\n",
    "widgetsnbextension==3.4.2\n",
    "wordcloud==1.5.0\n",
    "workalendar==2.5.0\n",
    "wrapt==1.10.11\n",
    "xgboost==0.71\n",
    "xlrd==1.1.0\n",
    "xmltodict==0.11.0\n",
    "zeep==3.3.1\n",
    "zope.interface==4.5.0\n",
    "\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Python能做什么"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "几年前 Python 在国内还只是一门小众语言。\n",
    "\n",
    "但从 2011开始到现在，Python 的百度搜索指数翻了10 倍，从下图看得出 Python 在国内的流行度已经直逼 Java，大有赶超之势\n",
    "<img src='./image/pcj.png' />"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "目前从热力图来看，中国是使用python最热的国家之一：\n",
    "<img src='./image/firerange.png' />"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "那么国内这几年 Python 怎么突然就火起来了？\n",
    "\n",
    "究其原因，大概可以从分3个阶段来分析。\n",
    "\n",
    "12~14年是云计算最火的几年，大批创业公司和巨头挤破头地进军云计算领域，大家都在做IAAS，最著名的云计算开源平台OpenStack 就是基于Python 开发的，为此催生出不少Python 岗位\n",
    "\n",
    "14~15年是「大众创新，万众创业」口号喊得最响两年，北京大街小巷似乎人人都是创业者，O2O、P2P产品如雨后春笋般冒出，什么语言最适合快速搭建原型？当然是Python，Python 的开发速度一个顶三。\n",
    "\n",
    "16~17年人工智能火遍大江南北，AlphaGO 的出现让业界为之兴奋，人工智能不再是概念，而人工智能、机器学习的首选语言就是Python。\n",
    "<img src='./image/mlp.jpeg' />\n",
    "这两年特别是网络爬虫火得一塌糊涂，10个写爬虫的9个在用Python，曾经有一段时间知乎的Python话题全部被爬虫相关的帖子刷屏，为什么爬虫这么火，这个还是跟大数据有关，因为数据挖掘、分析、机器学习、人工智能都需要大数据的支撑，而真正有大数据的厂商没几个，所以小厂不得不通过爬虫去获取数据。\n",
    "\n",
    "为什么偏偏就 Python 火了，其它语言没有？\n",
    "\n",
    "Ruby on Rails 最火的那几年，有人问Python 之父吉多·范罗苏姆怎么看，他说：\"Python 不需要杀手级应用，因为如果是这样的话，Python 在其它方面的作用就被忽略了。\"\n",
    "\n",
    "Python 在网络爬虫、数据分析、AI、机器学习、Web开发、金融、运维、测试等多个领域都有不俗的表现，从来没有哪一种语言可以同时在这么多领域扎根"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 开始Python的开发学习之旅。。。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 编码字符集\n",
    "默认情况下，Python3 源码文件以UTF-8编码，所有字符串都是unicode字符串。当然也可以为源文件指定不同的编码"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# -*- coding: cp-1252 -*-"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# -*- coding: utf-8 -*-"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 标识符\n",
    "1. 第一个字符必须是字母表中字母或下划线 _ 。\n",
    "2. 标识符的其他的部分由字母、数字和下划线组成。\n",
    "3. 标识符对大小写敏感。\n",
    "4. 非ASCII标识符也是允许的了"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "32支参赛队\n",
      "A\n"
     ]
    },
    {
     "ename": "NameError",
     "evalue": "name 'ASHARE' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-8-59e03a856202>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m世界杯2018\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mashare\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mASHARE\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'ASHARE' is not defined"
     ]
    }
   ],
   "source": [
    "世界杯2018 = '32支参赛队'\n",
    "ashare = 'A'\n",
    "b = 5\n",
    "print(世界杯2018)\n",
    "print(ashare)\n",
    "print(ASHARE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# python保留字\n",
    "保留字即关键字，我们不能把它们用作任何标识符名称。Python 的标准库提供了一个 keyword 模块，可以输出当前版本的所有关键字"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['False', 'None', 'True', 'and', 'as', 'assert', 'break', 'class', 'continue', 'def', 'del', 'elif', 'else', 'except', 'finally', 'for', 'from', 'global', 'if', 'import', 'in', 'is', 'lambda', 'nonlocal', 'not', 'or', 'pass', 'raise', 'return', 'try', 'while', 'with', 'yield']\n"
     ]
    }
   ],
   "source": [
    "import keyword\n",
    "print(keyword.kwlist)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 注释\n",
    "1. 单行注释以#开头\n",
    "2. 多行注释可以有多个#号，还有'''和\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 第一个注释\n",
    "# 第二个注释\n",
    "'''\n",
    "You know nothing, Jon Snow.\n",
    "'''\n",
    "winter = \"\"\"\n",
    "Winter is coming!\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Winter is coming!\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(winter)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 行与缩进\n",
    "1. python最具特色的就是使用缩进来表示代码块，不需要使用大括号 {} 。\n",
    "2. 缩进的空格数是可变的，但是同一个代码块的语句必须包含相同的缩进空格数\n",
    "3. 缩进必须一致，否则会报错"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "if True:\n",
    "    print('True')\n",
    "else:\n",
    "    print(\"False\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "ename": "IndentationError",
     "evalue": "expected an indented block (<ipython-input-19-06ec62a09bbf>, line 2)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-19-06ec62a09bbf>\"\u001b[1;36m, line \u001b[1;32m2\u001b[0m\n\u001b[1;33m    print('True')\u001b[0m\n\u001b[1;37m        ^\u001b[0m\n\u001b[1;31mIndentationError\u001b[0m\u001b[1;31m:\u001b[0m expected an indented block\n"
     ]
    }
   ],
   "source": [
    "if True:\n",
    "print('True')\n",
    "else:\n",
    "    print(\"False\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 多行语句\n",
    "可以使用反斜杠\\来实现多行语句。\n",
    "学会这个很重要，否则代码太长很难读"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6\n"
     ]
    }
   ],
   "source": [
    "a, b, c = 1, 2, 3\n",
    "total = a + \\\n",
    "        b + \\\n",
    "        c\n",
    "total = (a+\n",
    "         b+\n",
    "         c)\n",
    "print(total)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在 [], {}, 或 () 中的多行语句，不需要使用反斜杠(\\\\)，一般每一行末尾是逗号\n",
    "```\n",
    "name = 'Tom'\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['itemone', 'itemtwo', 'itemthree']\n"
     ]
    }
   ],
   "source": [
    "total = ['itemone',\n",
    "         'itemtwo',\n",
    "         'itemthree']\n",
    "print(total)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wwww\n",
      "wwww\n",
      "wwww\\nwwww\n"
     ]
    }
   ],
   "source": [
    "what = 'wwww\\nwwww'\n",
    "print(what)\n",
    "# raw\n",
    "what = r'wwww\\nwwww'\n",
    "print(what)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数字(number)类型\n",
    "python中数字有四种类型：整数、布尔型、浮点数和复数。\n",
    "\n",
    "1. int (整数), 如 1, 只有一种整数类型 int，表示为长整型，没有 python2 中的 Long。\n",
    "2. bool (布尔), 如 True。\n",
    "3. float (浮点数), 如 1.23、3E-2\n",
    "4. complex (复数), 如 1 + 2j、 1.1 + 2.2j"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'bool'>\n"
     ]
    }
   ],
   "source": [
    "isok = True\n",
    "print(type(isok))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 字符串 (string)\n",
    "1. python中单引号和双引号使用完全相同。\n",
    "2. 使用三引号('''或\"\"\")可以指定一个多行字符串。\n",
    "3. 转义符 '\\\\'\n",
    "4. 反斜杠可以用来转义，使用r可以让反斜杠不发生转义。。 如 r\"this is a line with \\\\n\" 则\\\\n会显示，并不是换行。\n",
    "5. 按字面意义级联字符串，如\"this \" \"is \" \"string\"会被自动转换为this is string。\n",
    "6. 字符串可以用 + 运算符连接在一起，用 * 运算符重复。\n",
    "7. Python 中的字符串有两种索引方式，从左往右以 0 开始，从右往左以 -1 开始。\n",
    "8. Python中的字符串不能改变。\n",
    "9. Python 没有单独的字符类型(char)，一个字符就是长度为 1 的字符串。\n",
    "10. 字符串的截取的语法格式如下：变量[头下标:尾下标]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "字符串 \n",
      " 这是一个句子。 \n",
      " \n",
      "这是一个段落，\n",
      "可以由多行组成\n",
      "字符串 这是一个句子。 \n",
      "这是一个段落，\n",
      "可以由多行组成\n"
     ]
    }
   ],
   "source": [
    "word = '字符串'\n",
    "sentence = \"这是一个句子。\"\n",
    "paragraph = \"\"\"\n",
    "这是一个段落，\n",
    "可以由多行组成\"\"\"\n",
    "print(word, '\\n', sentence, '\\n', paragraph)\n",
    "print('{0} {1} {2}'.format(word, sentence, paragraph))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Runoob\n",
      "Runoo\n",
      "R\n",
      "noo\n",
      "noob\n",
      "RunoobRunoobRunoobRunoobRunoobRunoobRunoobRunoobRunoobRunoob\n",
      "Runoob你好\n",
      "a: hello\n",
      "------------------------------\n",
      "hello\n",
      "runoob\n",
      "hello\\nrunoob\n"
     ]
    }
   ],
   "source": [
    "str='Runoob'\n",
    " \n",
    "print(str)                 # 输出字符串\n",
    "print(str[0:-1])           # 输出第一个到倒数第二个的所有字符\n",
    "print(str[0])              # 输出字符串第一个字符\n",
    "print(str[2:5])            # 输出从第三个开始到第五个的字符\n",
    "print(str[2:])             # 输出从第三个开始的后的所有字符\n",
    "print(str * 10)             # 输出字符串两次\n",
    "print(str + '你好')        # 连接字符串\n",
    "\n",
    "str = \"a: {0}\".format('hello')\n",
    "print(str)\n",
    "print('------------------------------')\n",
    " \n",
    "print('hello\\nrunoob')      # 使用反斜杠(\\)+n转义特殊字符\n",
    "print(r'hello\\nrunoob')     # 在字符串前面添加一个 r，表示原始字符串，不会发生转义"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 空行\n",
    "1. 函数之间或类的方法之间用空行分隔，表示一段新的代码的开始。类和函数入口之间也用一行空行分隔，以突出函数入口的开始。\n",
    "\n",
    "2. 空行与代码缩进不同，空行并不是Python语法的一部分。书写时不插入空行，Python解释器运行也不会出错。但是空行的作用在于分隔两段不同功能或含义的代码，<b>便于日后代码的维护或重构。</b>\n",
    "\n",
    "3. 记住：空行也是程序代码的一部分。\n",
    "4. 一般方法与方法之间，建议空两行"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 等待用户输入\n",
    "执行下面的程序在按回车键后就会等待用户输入"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "按下enter键后退出Tom\n",
      "Tom\n"
     ]
    }
   ],
   "source": [
    "a = input(\"\\n\\n按下enter键后退出\")\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 同一行显示多条语句\n",
    "Python可以在同一行中使用多条语句，语句之间使用分号(;)分割，以下是一个简单的实例\n",
    "## <font color = 'red'>不建议这样做，可读性差，单行长度过长，不容易维护</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "runoob\n"
     ]
    }
   ],
   "source": [
    "import sys; x = 'runoob'; sys.stdout.write(x + '\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 多个语句构成代码组\n",
    "1. 缩进相同的一组语句构成一个代码块，我们称之代码组。\n",
    "2. 像if、while、def和class这样的复合语句，首行以关键字开始，以冒号( : )结束，该行之后的一行或多行代码构成代码组。\n",
    "3. 我们将首行及后面的代码组称为一个子句(clause)。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False\n",
      "True\n",
      "20 > 0\n"
     ]
    }
   ],
   "source": [
    "condition1 = 0 > 20\n",
    "condition2 = 20 > 0\n",
    "print(condition1)\n",
    "print(condition2)\n",
    "if condition1 : \n",
    "    print('0 > 20')\n",
    "elif condition2 : \n",
    "    print('20 > 0')\n",
    "else : \n",
    "    print('no result')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# print输出\n",
    "print 默认输出是换行的，<font color='red'>如果要实现不换行需要在变量末尾加上 end=\"\"</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a\n",
      "b\n",
      "---------\n",
      "a b a b\n",
      "a b\n"
     ]
    }
   ],
   "source": [
    "x=\"a\"\n",
    "y=\"b\"\n",
    "# 换行输出\n",
    "print( x )\n",
    "print( y )\n",
    " \n",
    "print('---------')\n",
    "# 不换行输出\n",
    "print( x, end=\" \" )\n",
    "print( y, end=\" \" )\n",
    "print(x, y)\n",
    "print('{0} {1}'.format(x, y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# import 与 from...import\n",
    "1. 在 python 用 import 或者 from...import 来导入相应的模块。\n",
    "2. 将整个模块(somemodule)导入，格式为： import somemodule\n",
    "3. 从某个模块中导入某个函数,格式为： from somemodule import somefunction\n",
    "4. 从某个模块中导入多个函数,格式为： from somemodule import firstfunc, secondfunc, thirdfunc\n",
    "5. 将某个模块中的全部函数导入，格式为： from somemodule import *\n",
    "6. 对于常用的组件，如Numpy, Pandas, 一般都是用\n",
    "```\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "```\n",
    "7. 对于机器学习的一些库，因为类特别多，所以使用from...import比较多，如：\n",
    "```\n",
    "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier \n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 命令行参数\n",
    "很多程序可以执行一些操作来查看一些基本信息，Python可以使用-h参数查看各参数帮助信息\n",
    "```\n",
    "$ python -h\n",
    "usage: python [option] ... [-c cmd | -m mod | file | -] [arg] ...\n",
    "Options and arguments (and corresponding environment variables):\n",
    "-c cmd : program passed in as string (terminates option list)\n",
    "-d     : debug output from parser (also PYTHONDEBUG=x)\n",
    "-E     : ignore environment variables (such as PYTHONPATH)\n",
    "-h     : print this help message and exit\n",
    "\n",
    "[ etc. ]\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "但是在代码中，如果想看某个函数的说明，可以使用help(函数名)的方式，如"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on class list in module builtins:\n",
      "\n",
      "class list(object)\n",
      " |  list() -> new empty list\n",
      " |  list(iterable) -> new list initialized from iterable's items\n",
      " |  \n",
      " |  Methods defined here:\n",
      " |  \n",
      " |  __add__(self, value, /)\n",
      " |      Return self+value.\n",
      " |  \n",
      " |  __contains__(self, key, /)\n",
      " |      Return key in self.\n",
      " |  \n",
      " |  __delitem__(self, key, /)\n",
      " |      Delete self[key].\n",
      " |  \n",
      " |  __eq__(self, value, /)\n",
      " |      Return self==value.\n",
      " |  \n",
      " |  __ge__(self, value, /)\n",
      " |      Return self>=value.\n",
      " |  \n",
      " |  __getattribute__(self, name, /)\n",
      " |      Return getattr(self, name).\n",
      " |  \n",
      " |  __getitem__(...)\n",
      " |      x.__getitem__(y) <==> x[y]\n",
      " |  \n",
      " |  __gt__(self, value, /)\n",
      " |      Return self>value.\n",
      " |  \n",
      " |  __iadd__(self, value, /)\n",
      " |      Implement self+=value.\n",
      " |  \n",
      " |  __imul__(self, value, /)\n",
      " |      Implement self*=value.\n",
      " |  \n",
      " |  __init__(self, /, *args, **kwargs)\n",
      " |      Initialize self.  See help(type(self)) for accurate signature.\n",
      " |  \n",
      " |  __iter__(self, /)\n",
      " |      Implement iter(self).\n",
      " |  \n",
      " |  __le__(self, value, /)\n",
      " |      Return self<=value.\n",
      " |  \n",
      " |  __len__(self, /)\n",
      " |      Return len(self).\n",
      " |  \n",
      " |  __lt__(self, value, /)\n",
      " |      Return self<value.\n",
      " |  \n",
      " |  __mul__(self, value, /)\n",
      " |      Return self*value.n\n",
      " |  \n",
      " |  __ne__(self, value, /)\n",
      " |      Return self!=value.\n",
      " |  \n",
      " |  __new__(*args, **kwargs) from builtins.type\n",
      " |      Create and return a new object.  See help(type) for accurate signature.\n",
      " |  \n",
      " |  __repr__(self, /)\n",
      " |      Return repr(self).\n",
      " |  \n",
      " |  __reversed__(...)\n",
      " |      L.__reversed__() -- return a reverse iterator over the list\n",
      " |  \n",
      " |  __rmul__(self, value, /)\n",
      " |      Return self*value.\n",
      " |  \n",
      " |  __setitem__(self, key, value, /)\n",
      " |      Set self[key] to value.\n",
      " |  \n",
      " |  __sizeof__(...)\n",
      " |      L.__sizeof__() -- size of L in memory, in bytes\n",
      " |  \n",
      " |  append(...)\n",
      " |      L.append(object) -> None -- append object to end\n",
      " |  \n",
      " |  clear(...)\n",
      " |      L.clear() -> None -- remove all items from L\n",
      " |  \n",
      " |  copy(...)\n",
      " |      L.copy() -> list -- a shallow copy of L\n",
      " |  \n",
      " |  count(...)\n",
      " |      L.count(value) -> integer -- return number of occurrences of value\n",
      " |  \n",
      " |  extend(...)\n",
      " |      L.extend(iterable) -> None -- extend list by appending elements from the iterable\n",
      " |  \n",
      " |  index(...)\n",
      " |      L.index(value, [start, [stop]]) -> integer -- return first index of value.\n",
      " |      Raises ValueError if the value is not present.\n",
      " |  \n",
      " |  insert(...)\n",
      " |      L.insert(index, object) -- insert object before index\n",
      " |  \n",
      " |  pop(...)\n",
      " |      L.pop([index]) -> item -- remove and return item at index (default last).\n",
      " |      Raises IndexError if list is empty or index is out of range.\n",
      " |  \n",
      " |  remove(...)\n",
      " |      L.remove(value) -> None -- remove first occurrence of value.\n",
      " |      Raises ValueError if the value is not present.\n",
      " |  \n",
      " |  reverse(...)\n",
      " |      L.reverse() -- reverse *IN PLACE*\n",
      " |  \n",
      " |  sort(...)\n",
      " |      L.sort(key=None, reverse=False) -> None -- stable sort *IN PLACE*\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data and other attributes defined here:\n",
      " |  \n",
      " |  __hash__ = None\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on built-in function print in module builtins:\n",
      "\n",
      "print(...)\n",
      "    print(value, ..., sep=' ', end='\\n', file=sys.stdout, flush=False)\n",
      "    \n",
      "    Prints the values to a stream, or to sys.stdout by default.\n",
      "    Optional keyword arguments:\n",
      "    file:  a file-like object (stream); defaults to the current sys.stdout.\n",
      "    sep:   string inserted between values, default a space.\n",
      "    end:   string appended after the last value, default a newline.\n",
      "    flush: whether to forcibly flush the stream.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(print)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[['aaa', 'bbb', 'werer']]\n",
      "[['aaa', 'bbb', 'werer'], ['bb', 'cc', 'www']]\n"
     ]
    }
   ],
   "source": [
    "a = []\n",
    "a.append(['aaa', 'bbb', 'werer'])\n",
    "print(a)\n",
    "a.append(['bb', 'cc', 'www'])\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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