03-python-intro.ipynb

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   "source": [
    "\n",
    "# 第二章 数据科学的编程工具\n",
    "Python使用简介\n",
    "\n",
    "\n",
    "王成军\n",
    "\n",
    "![](./images/author.png)"
   ]
  },
  {
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   "source": [
    "## 人生苦短，我用Python。\n",
    "\n",
    "Python（/ˈpaɪθən/）是一种面向对象、解释型计算机程序设计语言\n",
    "- 由Guido van Rossum于1989年底发明\n",
    "- 第一个公开发行版发行于1991年\n",
    "- Python语法简洁而清晰\n",
    "- 具有强大的标准库和丰富的第三方模块\n",
    "- 它常被昵称为胶水语言\n",
    "- TIOBE编程语言排行榜“2010年度编程语言”\n",
    "\n"
   ]
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   "source": [
    "## 特点\n",
    "- 免费、功能强大、使用者众多\n",
    "- 与R和MATLAB相比，Python是一门更易学、更严谨的程序设计语言。使用Python编写的脚本更易于理解和维护。\n",
    "- 如同其它编程语言一样，Python语言的基础知识包括：类型、列表（list）和元组（tuple）、字典（dictionary）、条件、循环、异常处理等。\n",
    "- 关于这些，初阶读者可以阅读《Beginning Python》一书（Hetland, 2005)。\n"
   ]
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   "source": [
    "## Python中包含了丰富的类库。\n",
    "众多开源的科学计算软件包都提供了Python的调用接口，例如著名的计算机视觉库OpenCV。\n",
    "Python本身的科学计算类库发展也十分完善，例如NumPy、SciPy和matplotlib等。\n",
    "就社会网络分析而言，igraph, networkx, graph-tool, Snap.py等类库提供了丰富的网络分析工具"
   ]
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  {
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   "source": [
    "## Python软件与IDE\n",
    "目前最新的Python版本为3.0，更稳定的2.7版本。\n",
    "编译器是编写程序的重要工具。\n",
    "免费的Python编译器有Spyder、PyCharm(免费社区版)、Ipython、Vim、 Emacs、 Eclipse(加上PyDev插件)。\n"
   ]
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   "source": [
    "## Installing Anaconda Python\n",
    "- Use the Anaconda Python\n",
    "    - http://anaconda.com/"
   ]
  },
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   "source": [
    "## 第三方包可以使用pip install的方法安装。\n",
    "- 可以点击ToolsOpen command prompt\n",
    "- 然后在打开的命令窗口中输入：\n",
    "    - pip install RISE \n"
   ]
  },
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   "source": [
    "> pip install RISE"
   ]
  },
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   "source": [
    "- NumPy /SciPy for scientific computing\n",
    "- pandas to make Python usable for data analysis\n",
    "- matplotlib to make graphics\n",
    "- scikit-learn for machine learning\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-10T09:11:23.977691Z",
     "start_time": "2024-10-10T09:11:16.270074Z"
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   "outputs": [
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting flownetwork\n",
      "  Downloading flownetwork-3.1.0.tar.gz (125 kB)\n",
      "Collecting peppercorn\n",
      "  Downloading peppercorn-0.6-py3-none-any.whl (4.8 kB)\n",
      "Building wheels for collected packages: flownetwork\n",
      "  Building wheel for flownetwork (setup.py): started\n",
      "  Building wheel for flownetwork (setup.py): finished with status 'done'\n",
      "  Created wheel for flownetwork: filename=flownetwork-3.1.0-py2.py3-none-any.whl size=20719 sha256=b073ccae109b5afe6485d38ba3857e32cbef286795a80535bcae112d72f09288\n",
      "  Stored in directory: c:\\users\\administrator\\appdata\\local\\pip\\cache\\wheels\\22\\a7\\90\\ba01f3407a931c29a7770473a019040f32fefe04b0f148829c\n",
      "Successfully built flownetwork\n",
      "Installing collected packages: peppercorn, flownetwork\n",
      "Successfully installed flownetwork-3.1.0 peppercorn-0.6\n"
     ]
    }
   ],
   "source": [
    "!pip install flownetwork"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-11T02:22:37.975328Z",
     "start_time": "2024-10-11T02:22:19.441807Z"
    },
    "slideshow": {
     "slide_type": "skip"
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: statsmodels in c:\\programdata\\anaconda3\\lib\\site-packages (0.12.2)\n",
      "Collecting statsmodels\n",
      "  Downloading statsmodels-0.14.1-cp38-cp38-win_amd64.whl (10.0 MB)\n",
      "Requirement already satisfied: numpy<2,>=1.18 in c:\\users\\administrator\\appdata\\roaming\\python\\python38\\site-packages (from statsmodels) (1.24.4)\n",
      "Requirement already satisfied: scipy!=1.9.2,>=1.4 in c:\\users\\administrator\\appdata\\roaming\\python\\python38\\site-packages (from statsmodels) (1.10.1)\n",
      "Requirement already satisfied: packaging>=21.3 in c:\\users\\administrator\\appdata\\roaming\\python\\python38\\site-packages (from statsmodels) (24.1)\n",
      "Collecting patsy>=0.5.4\n",
      "  Downloading patsy-0.5.6-py2.py3-none-any.whl (233 kB)\n",
      "Requirement already satisfied: pandas!=2.1.0,>=1.0 in c:\\users\\administrator\\appdata\\roaming\\python\\python38\\site-packages (from statsmodels) (1.5.3)\n",
      "Requirement already satisfied: python-dateutil>=2.8.1 in c:\\programdata\\anaconda3\\lib\\site-packages (from pandas!=2.1.0,>=1.0->statsmodels) (2.8.2)\n",
      "Requirement already satisfied: pytz>=2020.1 in c:\\programdata\\anaconda3\\lib\\site-packages (from pandas!=2.1.0,>=1.0->statsmodels) (2021.1)\n",
      "Requirement already satisfied: six in c:\\programdata\\anaconda3\\lib\\site-packages (from patsy>=0.5.4->statsmodels) (1.16.0)\n",
      "Installing collected packages: patsy, statsmodels\n",
      "Successfully installed patsy-0.5.6 statsmodels-0.14.1\n"
     ]
    }
   ],
   "source": [
    "!pip install --user --upgrade statsmodels \n",
    "#iching"
   ]
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     "start_time": "2024-10-11T02:23:03.494796Z"
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    "slideshow": {
     "slide_type": "subslide"
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   },
   "outputs": [],
   "source": [
    "from flownetwork import flownetwork as fn\n",
    "import networkx as nx\n",
    "import pylab as plt\n",
    "import numpy as np\n",
    "\n",
    "#print(fn.__version__)\n"
   ]
  },
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   "execution_count": 2,
   "metadata": {
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     "end_time": "2024-10-11T02:23:17.921378Z",
     "start_time": "2024-10-11T02:23:17.913414Z"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function constructFlowNetwork in module flownetwork.flownetwork:\n",
      "\n",
      "constructFlowNetwork(C)\n",
      "    C is an array of two dimentions, e.g., \n",
      "    C = np.array([[user1, item1], \n",
      "                  [user1, item2], \n",
      "                  [user2, item1], \n",
      "                  [user2, item3]])\n",
      "    Return a balanced flow network\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(fn.constructFlowNetwork)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
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     "end_time": "2024-10-11T02:23:18.807061Z",
     "start_time": "2024-10-11T02:23:18.800092Z"
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    "slideshow": {
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   },
   "outputs": [],
   "source": [
    "# constructing a flow network\n",
    "demo = fn.attention_data\n",
    "gd = fn.constructFlowNetwork(demo) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# drawing a demo network\n",
    "fig = plt.figure(figsize=(12, 8),facecolor='white')\n",
    "pos={0: np.array([ 0.2 ,  0.8]),\n",
    " 2: np.array([ 0.2,  0.2]),\n",
    " 1: np.array([ 0.4,  0.6]),\n",
    " 6: np.array([ 0.4,  0.4]),\n",
    " 4: np.array([ 0.7,  0.8]),\n",
    " 5: np.array([ 0.7,  0.5]),\n",
    " 3: np.array([ 0.7,  0.2 ]),\n",
    " 'sink': np.array([ 1,  0.5]),\n",
    " 'source': np.array([ 0,  0.5])}\n",
    "\n",
    "width=[float(d['weight']*1.2) for (u,v,d) in gd.edges(data=True)]\n",
    "edge_labels=dict([((u,v,),d['weight']) for u,v,d in gd.edges(data=True)])\n",
    "\n",
    "nx.draw_networkx_edge_labels(gd,pos,edge_labels=edge_labels, font_size = 15, alpha = .5)\n",
    "nx.draw_networkx(gd, pos, node_size = 3000, node_color = 'orange',\n",
    "        alpha = 0.2, width = width, edge_color='orange',style='solid')\n",
    "nx.draw_networkx_labels(gd,pos,font_size=18)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-11T02:25:06.523568Z",
     "start_time": "2024-10-11T02:25:06.516599Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'DiGraph with 9 nodes and 15 edges'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nx.info(gd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-11T02:25:07.617725Z",
     "start_time": "2024-10-11T02:25:07.602792Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[0., 5., 1., 0., 2., 1., 0., 0., 0.],\n",
       "        [0., 0., 0., 0., 1., 0., 0., 3., 1.],\n",
       "        [0., 0., 0., 1., 0., 0., 0., 0., 0.],\n",
       "        [0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
       "        [0., 0., 0., 0., 0., 3., 0., 0., 0.],\n",
       "        [0., 0., 0., 2., 0., 0., 2., 0., 0.],\n",
       "        [0., 0., 0., 2., 0., 0., 0., 0., 0.],\n",
       "        [0., 0., 0., 2., 0., 0., 0., 0., 1.],\n",
       "        [0., 0., 0., 2., 0., 0., 0., 0., 0.]])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# flow matrix\n",
    "m = fn.getFlowMatrix(gd)\n",
    "m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-11T02:25:09.578327Z",
     "start_time": "2024-10-11T02:25:09.570362Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "defaultdict(<function flownetwork.flownetwork.networkDissipate.<locals>.<lambda>()>,\n",
       "            {0: [0, 5, 5],\n",
       "             1: [0, 3, 2],\n",
       "             2: [2, 4, 1],\n",
       "             6: [1, 1, 1],\n",
       "             3: [2, 2, 0],\n",
       "             4: [2, 3, 0],\n",
       "             5: [2, 2, 0]})"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fn.networkDissipate(gd)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-10T09:14:00.554790Z",
     "start_time": "2024-10-10T09:13:59.990214Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Your birthday & your prediction time:  200308030630202410101713\n",
      "there is a changing predict! Also run changePredict()\n",
      "there is a changing predict! Also run changePredict()\n",
      "蹇 & 坎 \n",
      " 本卦:  蹇卦原文蹇。利西南，不利东北。利见大人，贞吉。象曰：山上有水，蹇。君子以反身修德。白话文解释蹇卦：筮遇此卦，利西南行，不利东北行。利见贵族王公，获吉祥之兆。《象辞》说：上卦为坎，坎为水；下卦为艮，艮为山，山石磷峋，水流曲折，是蹇卦的卦象。君子观此卦象，悟行道之不易，从而反求诸己，修养德行。\n",
      "\n",
      "《断易天机》解蹇卦坎上艮下，为兑宫四世卦。此卦利于向西南方向行，不利于向东北方向，又利见大人。\n",
      "\n",
      "北宋易学家邵雍解踏步难行，艰辛万苦；进退维谷，容忍待时。得此卦者，身心忧苦，举步维艰，宜守正道，不可妄动，涉险境者会有灾难。\n",
      "\n",
      "台湾国学大儒傅佩荣解时运：处于艰困，更加奋勉。财运：财不流通，难以得利。家宅：防水冲损；婚姻有悔。身体：足部有疾，小心保养。\n",
      "\n",
      "传统解卦这个卦是异卦（下艮上坎）相叠。坎为水，艮为山。山高水深，困难重重，人生险阻，见险而止，明哲保身，可谓智慧。蹇，跋行艰难。大象：前路险陷，后有高山，进退维谷，如跛者举步为艰。运势：多灾多难，进退两难，此时宜守正道，不可轻举妄动，动反招祸。事业：诸事开头难，坚持下去，会有好的结果。人生不易，首先得求助自己，修德养性。志高自信，奋勇进取。逢到困难，得求友人相助，行动不可冒险，要以柔克刚，待机候时，量力而行。经商：逐步起业，冒险行事，自寻苦恼。宜加强商业道德，真诚同他人合作，陷入困境时，不得见死不救，而应奋不顾身。联合行动，可活跃于市场竞争之中。求名：循序渐进，加强自身修道。居静观动，待变，伺机而动，能进则进。内心坦然，不可为追逐名利而冒险。与贤能结合，追随高人，必能转危为安。婚恋：不很顺利，不得急，宜慎重选择。但应有自知之明。决策：艰难不利。但可视为考验。奋进向前，充满信心，切勿自寻苦恼，交结贤人，相互帮助，坚守中正之道，可遇难呈祥，结得善果。\n",
      "\n",
      "台湾张铭仁解卦蹇：表示寒足之意，主凶象，四大难卦第三卦。冰天雪地中赤足而行，表示现在处境多么的艰辛困苦，却又不能放守不管，只能硬撑到底。此时虽是无奈，但也总有苦尽甘来的时刻。人事均很为难之时，事业、感情均有骑虎难下的困境。解释：跛足，执行困难。特性：幻想多，重情调，喜新厌旧，有设计​​研发才能，追求理想化的生活，不切实际，故内心易有挫折感。运势：衰运，多灾难，进退两难之处境，要自重而不可妄动，此时应守正道待时也。凡事杂乱，更防小人之害，绝不可涉险境，否则灾害必至。家运：六亲无情，有贫困、不幸之象，运差者，其险难脱，善者宜致力克服挽救。宜注意家人有重病、血光之厄运。疾病：病重，不可疏忽医治，注意肾、胃、手部四肢损伤。胎孕：胎不安，孕有灾。子女：子女手足情薄，六亲少靠，劳碌之命也。周转：难调，多不成也。买卖：失利，交易不成。等人：不会来。寻人：速报警帮寻，得贵人则可回。失物：搜索有困难阻滞而停止，不能寻回。外出：出门可能有难，利于西南行。如外出遇难，可得救助，转危为安。考试：落榜。诉讼：终必有险，更防血光。不宜起讼，动则招咎。求事：眼前毫无希望。改行：不宜。执意强行并非良策。开业：开业者不宜，有是非、损耗之象。\n",
      "\n",
      "初六爻辞初六。往蹇来誉。 象曰：往蹇来誉，宜待也。 白话文解释初六：出门艰难，归来安适。《象辞》说：出门艰难，归来安适，知难而退，坐待时机。\n",
      "\n",
      "北宋易学家邵雍解平：得此爻者，宜守旧安常。做官的稳步进取，好运将至。\n",
      "\n",
      "台湾国学大儒傅佩荣解时运：暂时退守，以待好运。财运：不可冒险，守本为宜。家宅：迁居不利；婚姻宜待。身体：有病初起，退而自养。\n",
      "\n",
      "初六变卦初六爻动变得周易第63卦：水火既济。这个卦是异卦（下离上坎）相叠。坎为水，离为火，水火相交，水在火上，水势压倒火势，救火大功告成。既，已经；济，成也。既济就是事情已经成功，但终将发生变故。\n",
      "\n",
      "六二爻辞六二。王臣蹇蹇，匪躬之故。象曰：王臣蹇蹇，终无尤也。白话文解释六二：王臣之所以屡犯艰难，并不是为自身私利。《象辞》说：王臣出以公心屡犯艰难，其自身始终没有过失。\n",
      "\n",
      "北宋易学家邵雍解凶：得此爻者，涉艰历险，营谋有阻。做官的宜保持忠贞，报效国家。\n",
      "\n",
      "台湾国学大儒傅佩荣解时运：劳碌不已，避开险难。财运：中途受阻，人财两失。家宅：方向不利；夫恐有难。身体：过劳之厄。\n",
      "\n",
      "六二变卦六二爻动变得周易第48卦：水风井。这个卦是异卦（下巽上坎）相叠。坎为水；巽为木。树木得水而蓬勃生长。人靠水井生活，水井由人挖掘而成。相互为养，井以水养人，经久不竭，人应取此德而勤劳自勉。\n",
      "\n",
      "九三爻辞九三。往蹇来反。象曰：往蹇来反，内喜之也。白话文解释九三：出门困难重重，归来笑逐颜开。《象辞》说：出门困难重重，归来笑逐颜开，这是发自内心的喜悦。\n",
      "\n",
      "北宋易学家邵雍解平：得此爻者，不宜进取，或有妻子之喜。做官的位高者会晋升。\n",
      "\n",
      "台湾国学大儒傅佩荣解时运：前进不易，且先退守。财运：货物不畅，转销内地。家宅：团聚之喜；重逢有缘。身体：多加保养。\n",
      "\n",
      "九三变卦九三爻动变得周易第8卦：水地比。这个卦是异卦（下坤上坎）相叠，坤为地，坎为水。水附大地，地纳河海，相互依赖，亲密无间。此卦与师卦完全相反，互为综卦。它阐述的是相亲相辅，宽宏无私，精诚团结的道理。\n",
      "\n",
      "六四爻辞六四。往蹇来连。象曰：往蹇来连，当位实也。白话文解释六四：出门步履艰难，归来时却有车可乘。《象辞》说：出门步履艰难，归来时却有车可乘，因为六四阴爻居阴位，像人才正当其位，德符其名。\n",
      "\n",
      "北宋易学家邵雍解凶：得此爻者，不良者会有争诉。做官的进升有机，求名望利皆有实而不虚。\n",
      "\n",
      "台湾国学大儒傅佩荣解时运：同心协力，可以过关。财运：客人皆来，当然有利。家宅：比邻而居；亲上加亲。身体：久病缠身，一时难愈。\n",
      "\n",
      "六四变卦六四爻动变得周易第31卦：泽山咸。这个卦是异卦（下艮上兑）相叠。艮为山；泽为水。兑柔在上，艮刚在下，水向下渗，柔上而刚下，交相感应。感则成。\n",
      "\n",
      "九五爻辞九五。大蹇朋来。象曰：大蹇朋来，以中节也。白话文解释九五：经历了很多艰难困苦，终予获得大利。《象辞》说：大难当前，得到友人相助，因为九五之爻居上卦中位，像人节操贞正自能获救。\n",
      "\n",
      "北宋易学家邵雍解吉：得此爻者，会得到贵人提举，无往不利。\n",
      "\n",
      "台湾国学大儒傅佩荣解时运：转危为安，另图发展。财运：货物太多，不易脱手。家宅：不宜居家。身体：众医会诊，才可治好。\n",
      "\n",
      "九五变卦九五爻动变得周易第15卦：地山谦。这个卦是异卦（下艮上坤）相叠，艮为山，坤为地。地面有山，地卑（低）而山高，是为内高外低，比喻功高不自居，名高不自誉，位高不自傲。这就是谦。\n",
      "\n",
      "上六爻辞上六。往蹇来硕，吉。利见大人。象曰：往蹇来硕，志在内也；利见大人，以从贵也。白话文解释上六：出门困难重重，归来欢喜跳跃。筮遇此爻，吉利，利于会见贵族王公。《象辞》说：出门困难重重，归来欢喜跳跃，说明志气高昂，奋勇取胜。爻辞说：利于会见贵族王公，说明追随贵人，必获福利。\n",
      "\n",
      "北宋易学家邵雍解吉：得此爻者，好运来到，营谋遂意。做官的会身居要职，读书人进取成名。\n",
      "\n",
      "台湾国学大儒傅佩荣解时运：大运将至，名利皆实。财运：货价高涨，保本获利。家宅：贵人相助；婚姻主贵。身体：良医出手。\n",
      "\n",
      "上六变卦上六爻动变得周易第53卦：风山渐。这个卦是异卦（下艮上巽）相叠。艮为山，巽为木。山上有木，逐渐成长，山也随着增高。这是逐渐进步的过程，所以称渐，渐即进，渐渐前进而不急速。\n",
      "\n",
      " \n",
      "                (O--__/\\__--O) \n",
      "(-------------(O---- |__|----O)----------------) \n",
      "(-----------(O-----/-|__|-\\------O)------------) \n",
      "         (-------(O-/_--_\\-O)-------) \n",
      "\n",
      "\n",
      " 变卦:  坎卦原文坎。习坎，有孚，维心亨，行有尚。象曰：水洊至，习坎。君子以常德行，习教事。白话文解释习坎卦：抓获俘虏，劝慰安抚他们，通泰。途中将得到帮助。《象辞》说：坎为永，水长流不滞，是坎卦的卦象。君子观此卦象，从而尊尚德行，取法于细水长流之象，学习教化人民的方法。\n",
      "\n",
      "《断易天机》解坎卦坎上坎下，为坎宫本位卦。坎为陷入、陷阱，为险难之境。此时应坚持信心，才能豁然贯通。\n",
      "\n",
      "北宋易学家邵雍解艰难危险，重险重陷；事多困阻，谨慎行事。得此卦者，运气不佳，多难危险，事多困阻，宜谨言慎行，退守保安。\n",
      "\n",
      "台湾国学大儒傅佩荣解时运：逐步升迁，随时防患。财运：财如流水，商运亨通。家宅：邻居营造；亲上加亲。身体：水泻之症，虔心祷告。\n",
      "\n",
      "传统解卦这个卦是同卦（下坎上坎）相叠。坎为水、为险，两坎相重，险上加险，险阻重重。一阳陷二阴。所幸阴虚阳实，诚信可豁然贯通。虽险难重重，却方能显人性光彩。大象：两水重叠，坎水为险，进固险，退亦险，进退两难。运势：危机重重，宜沉著应付，保持心境开朗，凡事莫与人争。事业：陷入重重艰难险阻之中，险况丛生。既不得冒险，也不可束手待毙，应以积极态度，努力创造条件，改变处境，化险为夷。务必实心实意，充满信心，不图侥幸，不辞艰险，宜静观待变，运用智慧，突破险境，转危为安。经商：十分不利，甚至陷入面临破产的境地，但内心应沉着踏实，保持头脑冷静，身陷险境而心不陷，勿轻举妄动，先求自保以观变，把握时机以脱险。求名：不为他人理解，怀才不遇，千万不可自暴自弃。不改初衷，泰然自若，循序渐进，终可为人理解和发现。婚恋：多遭不利，应冷静分析原因，积极改变自身条件。双方同舟共济，共渡难关。决策：不幸。然而是转运前的考验。只要能不因困境而失常，且不图侥幸，保持信心，勿自陷险境。镇定自若，不轻举妄动，洞察时机，必可转危为安，成出险之功。\n",
      "\n",
      "台湾张铭仁解卦水：表示陷溺被水淹灭之意，主凶象，四大难卦第二卦。困难已到来了，此时正在挣扎中，随时会被淹灭而破散。若能破釜沉舟、奋力一击，或许尚有转机，需积极找求贵人，才会有生机。解释：漂浮，被情所困。特性：不安定感，好冒险，投机，变换工作环境，爱情多困扰不顺。运势：危机重重，宜容忍自重，保持心境开朗，沉着应付，则可有脱险之机。吃得苦中苦，方为人上人，凡事莫与人争长短，否则有不幸灾害。家运：时运不济，陷于困境之中，不和，且有分裂之忧，凡事宜慎重行事。疾病：病重宜速治，否则难救，注意肾脏，膀胱生殖器病。胎孕：临产有虚惊，难产之象。子女：子女多劳苦，宜忍辱负重，会有出人头地的一天。子女宜防水厄之灾。周转：难成。买卖：亏损。等人：不来。寻人：此人因家庭不和或不得志而出走，速寻北方水边。失物：找不回来。有失窃可能，或沉在水底。因坎水有困难之意。外出：大胆外出，另辟新路，必有所成，且得到他人相助。考试：不理想。诉讼：有盗贼之讼，或田土之争，有理说不清，凶象。求事：不利。改行：不宜，应暂时守住本行。开业：开业者不利，有陷入困难之象。\n",
      "\n",
      "初六爻辞初六。习坎，入于坎窞。凶。象曰：习坎入坎，失道凶也。白话文解释初六：坎坑之中又有坎坑，陷入重坑之中，凶险。《象辞》说：“坎坑之中又有坎坑，陷入重坑当中”，不行坦途，偏要走险道，必招致灾殃。\n",
      "\n",
      "北宋易学家邵雍解凶：得此爻者，时运不佳，宜防跌入陷阱灾难之中。做官的须防小人。\n",
      "\n",
      "台湾国学大儒傅佩荣解时运：侥幸求名，反而受损。财运：贩卖失利，有去无回。家宅：不安之屋；婚姻小心。身体：求医失当，情况危急。\n",
      "\n",
      "初六变卦初六爻动变得周易第60卦：水泽节。这个卦是异卦（下兑上坎）相叠。兑为泽，坎为水。泽有水而流有限，多必溢于泽外。因此要有节度，故称节。节卦与涣卦相反，互为综卦，交相使用。天地有节度才能常新，国家有节度才能安稳，个人有节度才能完美。\n",
      "\n",
      "九二爻辞九二。坎有险，求小得。象曰：求小得，未出中也。白话文解释九二：坑坑坎坎，道有险阻。敢于行险道，或小有收获。《象辞》说：敢于行险道，或小有收获，因为九二之爻居下卦的中位，像人尚未偏离正道。\n",
      "\n",
      "北宋易学家邵雍解平：得此爻者，有小成就，女命或为侍妾，凶者防险难或生心腹血气之疾。做官的会有小的成就，大事不成。\n",
      "\n",
      "台湾国学大儒傅佩荣解时运：小试有利，不可图大。财运：小心经营，可有小利。家宅：防止河岸。身体：医治有效，但难全好。\n",
      "\n",
      "九二变卦九二爻动变得周易第8卦：水地比。这个卦是异卦（下坤上坎）相叠，坤为地，坎为水。水附大地，地纳河海，相互依赖，亲密无间。此卦与师卦完全相反，互为综卦。它阐述的是相亲相辅，宽宏无私，精诚团结的道理。\n",
      "\n",
      "六三爻辞六三。来之坎坎，险且枕，入于坎窞，勿用。象曰：来之坎坎，终无功也。白话文解释六三：向坑坎走来，这坑坎既险又深，陷入重坎之中，非常不利。《象辞》说：来到这多坎之地，终无功利。\n",
      "\n",
      "北宋易学家邵雍解凶：得此爻者，多坎坷，多争诉之事。做官的宜退守。\n",
      "\n",
      "台湾国学大儒傅佩荣解时运：守住困穷，以待未来。财运：航行受阻，暂时守成。家宅：填满坎陷；求婚不成。身体：不宜过劳。\n",
      "\n",
      "六三变卦六三爻动变得周易第48卦：水风井。这个卦是异卦（下巽上坎）相叠。坎为水；巽为木。树木得水而蓬勃生长。人靠水井生活，水井由人挖掘而成。相互为养，井以水养人，经久不竭，人应取此德而勤劳自勉。\n",
      "\n",
      "六四爻辞六四。樽酒簋贰，用缶，纳约自牖，终无咎。象曰：樽酒簋贰，刚柔际也。白话文解释六四：用铜樽盛酒，用圆簋盛饭。然而对于关押在坎窖里的犯人，只须用瓦盆子就行了，牢饭从天窗里送进取出，其人遭此噩运，但最后还是没有危险。《象辞》说：“平时享受美食美器，此时用瓦盆子吃牢饭”，爻象表明六四阴爻处于九五阳爻之卞，像人被强者所压，受此磨难。\n",
      "\n",
      "北宋易学家邵雍解平：得此爻者，多喜事，或有结婚之喜。做官的多饮食之乐。\n",
      "\n",
      "台湾国学大儒傅佩荣解时运：春风得意，欢宴嘉宾。财运：造酒之业，颇为顺利。家宅：节俭持家；婚姻吉祥。身体：最好祷告。\n",
      "\n",
      "六四变卦六四爻动变得周易第47卦：泽水困。这个卦是异卦（下坎上兑）相叠。兑为阴为泽喻悦；坎为阳为水喻险。泽水困，陷入困境，才智难以施展，仍坚守正道，自得其乐，必可成事，摆脱困境。\n",
      "\n",
      "九五爻辞九五。坎不盈，只既平，无咎。象曰：坎不盈，中未大也。白话文解释九五：坎坑虽没有填满，小山头却被锄平。没有灾难。《象辞》说：小山头虽锄平，但坎坑尚未填满，说明道路不平，未成坦途。\n",
      "\n",
      "北宋易学家邵雍解平：得此爻者，平淡无常，宜养精蓄锐。做官的没有危难，职位无忧。\n",
      "\n",
      "台湾国学大儒傅佩荣解时运：不可自大，功名有限。财运：不谈近利，考虑长远。家宅：景观可喜；门当户对。身体：平心静气。\n",
      "\n",
      "九五变卦九五爻动变得周易第7卦：地水师。这个卦是异卦（下坎上坤）相叠。“师”指军队。坎为水、为险；坤为地、为顺，喻寓兵于农。兵凶战危，用兵乃圣人不得已而为之，但它可以顺利无阻碍地解决矛盾，因为顺乎形势，师出有名，故能化凶为吉。\n",
      "\n",
      "上六爻辞上六。系用徵纆，寘于丛棘，三岁不得，凶。象曰：上六失道，凶三岁也。白话文解释上六：把犯人用绳索捆紧，投入周围有丛棘的监狱中，三年不得释放，这是凶险之事。《象辞》说：上六爻辞所描述的，正说明官吏违背正道，违法囚人，历时三年之久。\n",
      "\n",
      "北宋易学家邵雍解凶：得此爻者，须防牢狱之灾。做官的须防职权旁落之忧。\n",
      "\n",
      "台湾国学大儒傅佩荣解时运：意外之灾，小心牢狱。财运：纺织生意，三年有成。家宅：整修家园；良缘须待三年。身体：不易动弹，安心静养。\n",
      "\n",
      "上六变卦上六爻动变得周易第59卦：风水涣。这个卦是异卦（下坎上巽）相叠。风在水上行，推波助澜，四方流溢。涣，水流流散之意。象征组织和人心涣散，必须用积极的手段和方法克服，战胜弊端，挽救涣散，转危为安。\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import iching.iching as i\n",
    "\n",
    "i.predict(200308030630, 202410101713)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-11T02:25:56.830153Z",
     "start_time": "2024-10-11T02:25:56.822189Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "import random, datetime\n",
    "import numpy as np\n",
    "import pylab as plt # plot\n",
    "import matplotlib\n",
    "import statsmodels.api as sm\n",
    "from scipy.stats import norm\n",
    "from scipy.stats import pearsonr\n",
    "#!pip install iching"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-10T09:19:08.176907Z",
     "start_time": "2024-10-10T09:19:08.104228Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "with open('./data/the_republic_plato_gutenberg_pg1497.txt', 'r') as f:\n",
    "    lines = f.readlines()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-10T09:19:35.958755Z",
     "start_time": "2024-10-10T09:19:35.938843Z"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24692"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(lines) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-10T09:19:47.589223Z",
     "start_time": "2024-10-10T09:19:47.579267Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "list"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(lines)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-10T09:21:50.102827Z",
     "start_time": "2024-10-10T09:21:50.088858Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "59\n"
     ]
    }
   ],
   "source": [
    "book = lines[8524:]\n",
    "\n",
    "num = 0\n",
    "for i in book:\n",
    "    if 'bad' in i:\n",
    "        num+=1\n",
    "print(num)\n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Variable Type\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-10T09:24:00.781671Z",
     "start_time": "2024-10-10T09:24:00.772711Z"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# str, int, float, bool\n",
    "type('Socrates')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-10T09:24:10.625640Z",
     "start_time": "2024-10-10T09:24:10.606724Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type('Socrates')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-10T09:25:01.509429Z",
     "start_time": "2024-10-10T09:25:01.499474Z"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# int\n",
    "int('5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-10T09:25:35.910993Z",
     "start_time": "2024-10-10T09:25:35.896058Z"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7.1"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# float\n",
    "float(str(7.1))\n",
    "#str(7.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-10T09:26:56.753470Z",
     "start_time": "2024-10-10T09:26:56.739532Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "range(0, 10)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "range(10) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-10T09:28:00.428981Z",
     "start_time": "2024-10-10T09:28:00.413052Z"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n"
     ]
    }
   ],
   "source": [
    "for i in range(1,10+1):\n",
    "    print(i)\n",
    "# range(1, 10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## dir & help\n",
    "\n",
    "当你想要了解对象的详细信息时使用"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-10T09:28:39.224925Z",
     "start_time": "2024-10-10T09:28:39.209991Z"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['__add__',\n",
       " '__class__',\n",
       " '__contains__',\n",
       " '__delattr__',\n",
       " '__dir__',\n",
       " '__doc__',\n",
       " '__eq__',\n",
       " '__format__',\n",
       " '__ge__',\n",
       " '__getattribute__',\n",
       " '__getitem__',\n",
       " '__getnewargs__',\n",
       " '__gt__',\n",
       " '__hash__',\n",
       " '__init__',\n",
       " '__init_subclass__',\n",
       " '__iter__',\n",
       " '__le__',\n",
       " '__len__',\n",
       " '__lt__',\n",
       " '__mod__',\n",
       " '__mul__',\n",
       " '__ne__',\n",
       " '__new__',\n",
       " '__reduce__',\n",
       " '__reduce_ex__',\n",
       " '__repr__',\n",
       " '__rmod__',\n",
       " '__rmul__',\n",
       " '__setattr__',\n",
       " '__sizeof__',\n",
       " '__str__',\n",
       " '__subclasshook__',\n",
       " 'capitalize',\n",
       " 'casefold',\n",
       " 'center',\n",
       " 'count',\n",
       " 'encode',\n",
       " 'endswith',\n",
       " 'expandtabs',\n",
       " 'find',\n",
       " 'format',\n",
       " 'format_map',\n",
       " 'index',\n",
       " 'isalnum',\n",
       " 'isalpha',\n",
       " 'isascii',\n",
       " 'isdecimal',\n",
       " 'isdigit',\n",
       " 'isidentifier',\n",
       " 'islower',\n",
       " 'isnumeric',\n",
       " 'isprintable',\n",
       " 'isspace',\n",
       " 'istitle',\n",
       " 'isupper',\n",
       " 'join',\n",
       " 'ljust',\n",
       " 'lower',\n",
       " 'lstrip',\n",
       " 'maketrans',\n",
       " 'partition',\n",
       " 'replace',\n",
       " 'rfind',\n",
       " 'rindex',\n",
       " 'rjust',\n",
       " 'rpartition',\n",
       " 'rsplit',\n",
       " 'rstrip',\n",
       " 'split',\n",
       " 'splitlines',\n",
       " 'startswith',\n",
       " 'strip',\n",
       " 'swapcase',\n",
       " 'title',\n",
       " 'translate',\n",
       " 'upper',\n",
       " 'zfill']"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#dir(str)[-10:]\n",
    "dir(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-10T09:29:56.395598Z",
     "start_time": "2024-10-10T09:29:56.366725Z"
    },
    "scrolled": true,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on class str in module builtins:\n",
      "\n",
      "class str(object)\n",
      " |  str(object='') -> str\n",
      " |  str(bytes_or_buffer[, encoding[, errors]]) -> str\n",
      " |  \n",
      " |  Create a new string object from the given object. If encoding or\n",
      " |  errors is specified, then the object must expose a data buffer\n",
      " |  that will be decoded using the given encoding and error handler.\n",
      " |  Otherwise, returns the result of object.__str__() (if defined)\n",
      " |  or repr(object).\n",
      " |  encoding defaults to sys.getdefaultencoding().\n",
      " |  errors defaults to 'strict'.\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",
      " |  __eq__(self, value, /)\n",
      " |      Return self==value.\n",
      " |  \n",
      " |  __format__(self, format_spec, /)\n",
      " |      Return a formatted version of the string as described by format_spec.\n",
      " |  \n",
      " |  __ge__(self, value, /)\n",
      " |      Return self>=value.\n",
      " |  \n",
      " |  __getattribute__(self, name, /)\n",
      " |      Return getattr(self, name).\n",
      " |  \n",
      " |  __getitem__(self, key, /)\n",
      " |      Return self[key].\n",
      " |  \n",
      " |  __getnewargs__(...)\n",
      " |  \n",
      " |  __gt__(self, value, /)\n",
      " |      Return self>value.\n",
      " |  \n",
      " |  __hash__(self, /)\n",
      " |      Return hash(self).\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",
      " |  __mod__(self, value, /)\n",
      " |      Return self%value.\n",
      " |  \n",
      " |  __mul__(self, value, /)\n",
      " |      Return self*value.\n",
      " |  \n",
      " |  __ne__(self, value, /)\n",
      " |      Return self!=value.\n",
      " |  \n",
      " |  __repr__(self, /)\n",
      " |      Return repr(self).\n",
      " |  \n",
      " |  __rmod__(self, value, /)\n",
      " |      Return value%self.\n",
      " |  \n",
      " |  __rmul__(self, value, /)\n",
      " |      Return value*self.\n",
      " |  \n",
      " |  __sizeof__(self, /)\n",
      " |      Return the size of the string in memory, in bytes.\n",
      " |  \n",
      " |  __str__(self, /)\n",
      " |      Return str(self).\n",
      " |  \n",
      " |  capitalize(self, /)\n",
      " |      Return a capitalized version of the string.\n",
      " |      \n",
      " |      More specifically, make the first character have upper case and the rest lower\n",
      " |      case.\n",
      " |  \n",
      " |  casefold(self, /)\n",
      " |      Return a version of the string suitable for caseless comparisons.\n",
      " |  \n",
      " |  center(self, width, fillchar=' ', /)\n",
      " |      Return a centered string of length width.\n",
      " |      \n",
      " |      Padding is done using the specified fill character (default is a space).\n",
      " |  \n",
      " |  count(...)\n",
      " |      S.count(sub[, start[, end]]) -> int\n",
      " |      \n",
      " |      Return the number of non-overlapping occurrences of substring sub in\n",
      " |      string S[start:end].  Optional arguments start and end are\n",
      " |      interpreted as in slice notation.\n",
      " |  \n",
      " |  encode(self, /, encoding='utf-8', errors='strict')\n",
      " |      Encode the string using the codec registered for encoding.\n",
      " |      \n",
      " |      encoding\n",
      " |        The encoding in which to encode the string.\n",
      " |      errors\n",
      " |        The error handling scheme to use for encoding errors.\n",
      " |        The default is 'strict' meaning that encoding errors raise a\n",
      " |        UnicodeEncodeError.  Other possible values are 'ignore', 'replace' and\n",
      " |        'xmlcharrefreplace' as well as any other name registered with\n",
      " |        codecs.register_error that can handle UnicodeEncodeErrors.\n",
      " |  \n",
      " |  endswith(...)\n",
      " |      S.endswith(suffix[, start[, end]]) -> bool\n",
      " |      \n",
      " |      Return True if S ends with the specified suffix, False otherwise.\n",
      " |      With optional start, test S beginning at that position.\n",
      " |      With optional end, stop comparing S at that position.\n",
      " |      suffix can also be a tuple of strings to try.\n",
      " |  \n",
      " |  expandtabs(self, /, tabsize=8)\n",
      " |      Return a copy where all tab characters are expanded using spaces.\n",
      " |      \n",
      " |      If tabsize is not given, a tab size of 8 characters is assumed.\n",
      " |  \n",
      " |  find(...)\n",
      " |      S.find(sub[, start[, end]]) -> int\n",
      " |      \n",
      " |      Return the lowest index in S where substring sub is found,\n",
      " |      such that sub is contained within S[start:end].  Optional\n",
      " |      arguments start and end are interpreted as in slice notation.\n",
      " |      \n",
      " |      Return -1 on failure.\n",
      " |  \n",
      " |  format(...)\n",
      " |      S.format(*args, **kwargs) -> str\n",
      " |      \n",
      " |      Return a formatted version of S, using substitutions from args and kwargs.\n",
      " |      The substitutions are identified by braces ('{' and '}').\n",
      " |  \n",
      " |  format_map(...)\n",
      " |      S.format_map(mapping) -> str\n",
      " |      \n",
      " |      Return a formatted version of S, using substitutions from mapping.\n",
      " |      The substitutions are identified by braces ('{' and '}').\n",
      " |  \n",
      " |  index(...)\n",
      " |      S.index(sub[, start[, end]]) -> int\n",
      " |      \n",
      " |      Return the lowest index in S where substring sub is found,\n",
      " |      such that sub is contained within S[start:end].  Optional\n",
      " |      arguments start and end are interpreted as in slice notation.\n",
      " |      \n",
      " |      Raises ValueError when the substring is not found.\n",
      " |  \n",
      " |  isalnum(self, /)\n",
      " |      Return True if the string is an alpha-numeric string, False otherwise.\n",
      " |      \n",
      " |      A string is alpha-numeric if all characters in the string are alpha-numeric and\n",
      " |      there is at least one character in the string.\n",
      " |  \n",
      " |  isalpha(self, /)\n",
      " |      Return True if the string is an alphabetic string, False otherwise.\n",
      " |      \n",
      " |      A string is alphabetic if all characters in the string are alphabetic and there\n",
      " |      is at least one character in the string.\n",
      " |  \n",
      " |  isascii(self, /)\n",
      " |      Return True if all characters in the string are ASCII, False otherwise.\n",
      " |      \n",
      " |      ASCII characters have code points in the range U+0000-U+007F.\n",
      " |      Empty string is ASCII too.\n",
      " |  \n",
      " |  isdecimal(self, /)\n",
      " |      Return True if the string is a decimal string, False otherwise.\n",
      " |      \n",
      " |      A string is a decimal string if all characters in the string are decimal and\n",
      " |      there is at least one character in the string.\n",
      " |  \n",
      " |  isdigit(self, /)\n",
      " |      Return True if the string is a digit string, False otherwise.\n",
      " |      \n",
      " |      A string is a digit string if all characters in the string are digits and there\n",
      " |      is at least one character in the string.\n",
      " |  \n",
      " |  isidentifier(self, /)\n",
      " |      Return True if the string is a valid Python identifier, False otherwise.\n",
      " |      \n",
      " |      Call keyword.iskeyword(s) to test whether string s is a reserved identifier,\n",
      " |      such as \"def\" or \"class\".\n",
      " |  \n",
      " |  islower(self, /)\n",
      " |      Return True if the string is a lowercase string, False otherwise.\n",
      " |      \n",
      " |      A string is lowercase if all cased characters in the string are lowercase and\n",
      " |      there is at least one cased character in the string.\n",
      " |  \n",
      " |  isnumeric(self, /)\n",
      " |      Return True if the string is a numeric string, False otherwise.\n",
      " |      \n",
      " |      A string is numeric if all characters in the string are numeric and there is at\n",
      " |      least one character in the string.\n",
      " |  \n",
      " |  isprintable(self, /)\n",
      " |      Return True if the string is printable, False otherwise.\n",
      " |      \n",
      " |      A string is printable if all of its characters are considered printable in\n",
      " |      repr() or if it is empty.\n",
      " |  \n",
      " |  isspace(self, /)\n",
      " |      Return True if the string is a whitespace string, False otherwise.\n",
      " |      \n",
      " |      A string is whitespace if all characters in the string are whitespace and there\n",
      " |      is at least one character in the string.\n",
      " |  \n",
      " |  istitle(self, /)\n",
      " |      Return True if the string is a title-cased string, False otherwise.\n",
      " |      \n",
      " |      In a title-cased string, upper- and title-case characters may only\n",
      " |      follow uncased characters and lowercase characters only cased ones.\n",
      " |  \n",
      " |  isupper(self, /)\n",
      " |      Return True if the string is an uppercase string, False otherwise.\n",
      " |      \n",
      " |      A string is uppercase if all cased characters in the string are uppercase and\n",
      " |      there is at least one cased character in the string.\n",
      " |  \n",
      " |  join(self, iterable, /)\n",
      " |      Concatenate any number of strings.\n",
      " |      \n",
      " |      The string whose method is called is inserted in between each given string.\n",
      " |      The result is returned as a new string.\n",
      " |      \n",
      " |      Example: '.'.join(['ab', 'pq', 'rs']) -> 'ab.pq.rs'\n",
      " |  \n",
      " |  ljust(self, width, fillchar=' ', /)\n",
      " |      Return a left-justified string of length width.\n",
      " |      \n",
      " |      Padding is done using the specified fill character (default is a space).\n",
      " |  \n",
      " |  lower(self, /)\n",
      " |      Return a copy of the string converted to lowercase.\n",
      " |  \n",
      " |  lstrip(self, chars=None, /)\n",
      " |      Return a copy of the string with leading whitespace removed.\n",
      " |      \n",
      " |      If chars is given and not None, remove characters in chars instead.\n",
      " |  \n",
      " |  partition(self, sep, /)\n",
      " |      Partition the string into three parts using the given separator.\n",
      " |      \n",
      " |      This will search for the separator in the string.  If the separator is found,\n",
      " |      returns a 3-tuple containing the part before the separator, the separator\n",
      " |      itself, and the part after it.\n",
      " |      \n",
      " |      If the separator is not found, returns a 3-tuple containing the original string\n",
      " |      and two empty strings.\n",
      " |  \n",
      " |  replace(self, old, new, count=-1, /)\n",
      " |      Return a copy with all occurrences of substring old replaced by new.\n",
      " |      \n",
      " |        count\n",
      " |          Maximum number of occurrences to replace.\n",
      " |          -1 (the default value) means replace all occurrences.\n",
      " |      \n",
      " |      If the optional argument count is given, only the first count occurrences are\n",
      " |      replaced.\n",
      " |  \n",
      " |  rfind(...)\n",
      " |      S.rfind(sub[, start[, end]]) -> int\n",
      " |      \n",
      " |      Return the highest index in S where substring sub is found,\n",
      " |      such that sub is contained within S[start:end].  Optional\n",
      " |      arguments start and end are interpreted as in slice notation.\n",
      " |      \n",
      " |      Return -1 on failure.\n",
      " |  \n",
      " |  rindex(...)\n",
      " |      S.rindex(sub[, start[, end]]) -> int\n",
      " |      \n",
      " |      Return the highest index in S where substring sub is found,\n",
      " |      such that sub is contained within S[start:end].  Optional\n",
      " |      arguments start and end are interpreted as in slice notation.\n",
      " |      \n",
      " |      Raises ValueError when the substring is not found.\n",
      " |  \n",
      " |  rjust(self, width, fillchar=' ', /)\n",
      " |      Return a right-justified string of length width.\n",
      " |      \n",
      " |      Padding is done using the specified fill character (default is a space).\n",
      " |  \n",
      " |  rpartition(self, sep, /)\n",
      " |      Partition the string into three parts using the given separator.\n",
      " |      \n",
      " |      This will search for the separator in the string, starting at the end. If\n",
      " |      the separator is found, returns a 3-tuple containing the part before the\n",
      " |      separator, the separator itself, and the part after it.\n",
      " |      \n",
      " |      If the separator is not found, returns a 3-tuple containing two empty strings\n",
      " |      and the original string.\n",
      " |  \n",
      " |  rsplit(self, /, sep=None, maxsplit=-1)\n",
      " |      Return a list of the words in the string, using sep as the delimiter string.\n",
      " |      \n",
      " |        sep\n",
      " |          The delimiter according which to split the string.\n",
      " |          None (the default value) means split according to any whitespace,\n",
      " |          and discard empty strings from the result.\n",
      " |        maxsplit\n",
      " |          Maximum number of splits to do.\n",
      " |          -1 (the default value) means no limit.\n",
      " |      \n",
      " |      Splits are done starting at the end of the string and working to the front.\n",
      " |  \n",
      " |  rstrip(self, chars=None, /)\n",
      " |      Return a copy of the string with trailing whitespace removed.\n",
      " |      \n",
      " |      If chars is given and not None, remove characters in chars instead.\n",
      " |  \n",
      " |  split(self, /, sep=None, maxsplit=-1)\n",
      " |      Return a list of the words in the string, using sep as the delimiter string.\n",
      " |      \n",
      " |      sep\n",
      " |        The delimiter according which to split the string.\n",
      " |        None (the default value) means split according to any whitespace,\n",
      " |        and discard empty strings from the result.\n",
      " |      maxsplit\n",
      " |        Maximum number of splits to do.\n",
      " |        -1 (the default value) means no limit.\n",
      " |  \n",
      " |  splitlines(self, /, keepends=False)\n",
      " |      Return a list of the lines in the string, breaking at line boundaries.\n",
      " |      \n",
      " |      Line breaks are not included in the resulting list unless keepends is given and\n",
      " |      true.\n",
      " |  \n",
      " |  startswith(...)\n",
      " |      S.startswith(prefix[, start[, end]]) -> bool\n",
      " |      \n",
      " |      Return True if S starts with the specified prefix, False otherwise.\n",
      " |      With optional start, test S beginning at that position.\n",
      " |      With optional end, stop comparing S at that position.\n",
      " |      prefix can also be a tuple of strings to try.\n",
      " |  \n",
      " |  strip(self, chars=None, /)\n",
      " |      Return a copy of the string with leading and trailing whitespace removed.\n",
      " |      \n",
      " |      If chars is given and not None, remove characters in chars instead.\n",
      " |  \n",
      " |  swapcase(self, /)\n",
      " |      Convert uppercase characters to lowercase and lowercase characters to uppercase.\n",
      " |  \n",
      " |  title(self, /)\n",
      " |      Return a version of the string where each word is titlecased.\n",
      " |      \n",
      " |      More specifically, words start with uppercased characters and all remaining\n",
      " |      cased characters have lower case.\n",
      " |  \n",
      " |  translate(self, table, /)\n",
      " |      Replace each character in the string using the given translation table.\n",
      " |      \n",
      " |        table\n",
      " |          Translation table, which must be a mapping of Unicode ordinals to\n",
      " |          Unicode ordinals, strings, or None.\n",
      " |      \n",
      " |      The table must implement lookup/indexing via __getitem__, for instance a\n",
      " |      dictionary or list.  If this operation raises LookupError, the character is\n",
      " |      left untouched.  Characters mapped to None are deleted.\n",
      " |  \n",
      " |  upper(self, /)\n",
      " |      Return a copy of the string converted to uppercase.\n",
      " |  \n",
      " |  zfill(self, width, /)\n",
      " |      Pad a numeric string with zeros on the left, to fill a field of the given width.\n",
      " |      \n",
      " |      The string is never truncated.\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Static methods defined here:\n",
      " |  \n",
      " |  __new__(*args, **kwargs) from builtins.type\n",
      " |      Create and return a new object.  See help(type) for accurate signature.\n",
      " |  \n",
      " |  maketrans(...)\n",
      " |      Return a translation table usable for str.translate().\n",
      " |      \n",
      " |      If there is only one argument, it must be a dictionary mapping Unicode\n",
      " |      ordinals (integers) or characters to Unicode ordinals, strings or None.\n",
      " |      Character keys will be then converted to ordinals.\n",
      " |      If there are two arguments, they must be strings of equal length, and\n",
      " |      in the resulting dictionary, each character in x will be mapped to the\n",
      " |      character at the same position in y. If there is a third argument, it\n",
      " |      must be a string, whose characters will be mapped to None in the result.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-10T09:31:05.291968Z",
     "start_time": "2024-10-10T09:31:05.281016Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'cheng jun is a big fan of Socrates!'"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'cheng jun'.__add__(' is a big fan of Socrates!')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-14T03:55:38.921530Z",
     "start_time": "2021-05-14T03:55:38.917817Z"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'   '.isspace()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-14T03:53:49.045706Z",
     "start_time": "2021-05-14T03:53:49.042142Z"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'socrates the king is the greatest.'"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'socrates the king'.__add__(' is the greatest.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-10T09:32:31.286163Z",
     "start_time": "2024-10-10T09:32:31.272224Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['rstrip',\n",
       " 'split',\n",
       " 'splitlines',\n",
       " 'startswith',\n",
       " 'strip',\n",
       " 'swapcase',\n",
       " 'title',\n",
       " 'translate',\n",
       " 'upper',\n",
       " 'zfill']"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = ' Hello WorlD  '\n",
    "dir(x)[-10:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-10T09:32:42.694408Z",
     "start_time": "2024-10-10T09:32:42.688434Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "' hello world  '"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# lower\n",
    "x.lower() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-10T09:32:48.791902Z",
     "start_time": "2024-10-10T09:32:48.783938Z"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "' HELLO WORLD  '"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# upper\n",
    "x.upper()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-10T09:33:02.011317Z",
     "start_time": "2024-10-10T09:33:01.992403Z"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "' Hello WorlD'"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# rstrip\n",
    "x.rstrip()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-10T09:33:18.147009Z",
     "start_time": "2024-10-10T09:33:18.141036Z"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Hello WorlD'"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# strip\n",
    "x.strip()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-10T09:33:33.765741Z",
     "start_time": "2024-10-10T09:33:33.756780Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "' Hell WorlD  '"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# replace\n",
    "x.replace('lo', 'l')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-08T09:26:24.261618Z",
     "start_time": "2023-10-08T09:26:24.257934Z"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[' Hel', ' WorlD  ']"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# split\n",
    "# x.lower().strip().split(' ')\n",
    "x.split('lo')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-10T09:34:25.704576Z",
     "start_time": "2024-10-10T09:34:25.696612Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'a - 1'"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# join \n",
    "' - '.join(['a', '1'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## type\n",
    "当你想要了解变量类型时使用type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-10T09:35:53.331659Z",
     "start_time": "2024-10-10T09:35:53.313738Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = 'hello world'\n",
    "type(x)\n",
    "#help(type(x))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Data Structure\n",
    "list, tuple, set, dictionary, array\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-10T09:38:55.090075Z",
     "start_time": "2024-10-10T09:38:55.070163Z"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 2, 3, 3] (1, 2, 3, 3) {1, 2, 3} {'a': 1, 'b': 2, 'c': 3} [1 2 3 3]\n"
     ]
    }
   ],
   "source": [
    "l = [1,2,3,3] # list\n",
    "t = (1, 2, 3, 3) # tuple\n",
    "s = {1, 2, 3, 3} # set([1,2,3,3]) # set\n",
    "d = {'a':1,'b':2,'c':3} # dict\n",
    "a = np.array(l) # array\n",
    "print(l, t, s, d, a)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-10T09:40:18.005787Z",
     "start_time": "2024-10-10T09:40:17.997823Z"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3, 3, 4]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l = [1,2,3,3] # list\n",
    "l.append(4)\n",
    "l\n",
    "#help(list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-10T09:40:27.974635Z",
     "start_time": "2024-10-10T09:40:27.957710Z"
    },
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['a', 'b', 'c'])"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d = {'a':1,'b':2,'c':3} # dict\n",
    "d.keys()\n",
    "#help(dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-08T09:35:29.361343Z",
     "start_time": "2023-10-08T09:35:29.357425Z"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_values([1, 2, 3])"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d = {'a':1,'b':2,'c':3} # dict\n",
    "d.values() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-10T09:41:05.782125Z",
     "start_time": "2024-10-10T09:41:05.771657Z"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d = {3:1,'b':3,'c':1} # dict\n",
    "d['c']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-22T01:32:27.607384Z",
     "start_time": "2020-09-22T01:32:27.603091Z"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_items([('a', 1), ('b', 2), ('c', 3)])"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d = {'a':1,'b':2,'c':3} # dict\n",
    "d.items() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## 定义函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-10T09:44:38.613832Z",
     "start_time": "2024-10-10T09:44:38.602880Z"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "def devidePlus(m, n): # 结尾是冒号\n",
    "    y = m/n + 1 # 注意：空格\n",
    "    return y          # 注意：return"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## For 循环"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-08T09:40:20.114390Z",
     "start_time": "2023-10-08T09:40:20.110910Z"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "range(0, 10)"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "range(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-10-11T07:44:33.929631Z",
     "start_time": "2021-10-11T07:44:33.923987Z"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "range(1, 10)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "range(1, 10)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-08T09:40:37.430778Z",
     "start_time": "2023-10-08T09:40:37.426967Z"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 0 0\n",
      "1 10 1\n",
      "2 20 4\n",
      "3 30 9\n",
      "4 40 16\n",
      "5 50 25\n",
      "6 60 36\n",
      "7 70 49\n",
      "8 80 64\n",
      "9 90 81\n"
     ]
    }
   ],
   "source": [
    "for i in range(10):\n",
    "    print(i, i*10, i**2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-10-11T07:45:20.900095Z",
     "start_time": "2021-10-11T07:45:20.890918Z"
    },
    "code_folding": [],
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "# for i in range(10):\n",
    "#     print(i*10) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-10T09:46:40.994431Z",
     "start_time": "2024-10-10T09:46:40.985471Z"
    },
    "scrolled": true,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n",
      "1.5\n",
      "2.0\n",
      "2.5\n",
      "3.0\n",
      "3.5\n",
      "4.0\n",
      "4.5\n",
      "5.0\n",
      "5.5\n"
     ]
    }
   ],
   "source": [
    "for i in range(10):\n",
    "    print(devidePlus(i, 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-10T09:47:24.701598Z",
     "start_time": "2024-10-10T09:47:24.693634Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, 5.0, 5.5]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 列表内部的for循环（列表推演）\n",
    "r = [devidePlus(i, 2) for i in range(10)] \n",
    "r "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## map函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-10T09:48:34.418166Z",
     "start_time": "2024-10-10T09:48:34.400246Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "32.0 72.5 104.0 212.0\n"
     ]
    }
   ],
   "source": [
    "def fahrenheit(T):\n",
    "    return (9/5)*T + 32\n",
    "\n",
    "temp = [0, 22.5, 40,100]\n",
    "\n",
    "#[fahrenheit(i) for i in temp]\n",
    "F_temps = map(fahrenheit, temp)\n",
    "print(*F_temps)\n",
    "#[i for i in F_temps]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-10-11T07:49:15.341189Z",
     "start_time": "2021-10-11T07:49:15.325415Z"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.0 4.0 1.4\n"
     ]
    }
   ],
   "source": [
    "m1 = map(devidePlus, [4,3,2], [2, 1, 5])\n",
    "print(*m1)\n",
    "#print(*map(devidePlus, [4,3,2], [2, 1, 5]))\n",
    "# 注意： 将（4， 2)作为一个组合进行计算，将（3， 1）作为一个组合进行计算"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-10T09:49:38.177451Z",
     "start_time": "2024-10-10T09:49:38.165504Z"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3 7 11 15 19\n"
     ]
    }
   ],
   "source": [
    "m2 = map(lambda x, y: x + y, [1, 3, 5, 7, 9], [2, 4, 6, 8, 10])\n",
    "print(*m2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-10T09:49:55.962632Z",
     "start_time": "2024-10-10T09:49:55.945706Z"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 4 9 13 14\n"
     ]
    }
   ],
   "source": [
    "m3 = map(lambda x, y, z: x + y - z, [1, 3, 5, 7, 9], [2, 4, 6, 8, 10], [3, 3, 2, 2, 5])\n",
    "print(*m3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## if elif else"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-10T09:51:07.100693Z",
     "start_time": "2024-10-10T09:51:07.092728Z"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "余数既不是1也不是0\n"
     ]
    }
   ],
   "source": [
    "j = 5.5\n",
    "if j%2 == 1:\n",
    "    print(r'余数是1')\n",
    "elif j%2 ==0:\n",
    "    print(r'余数是0')\n",
    "else:\n",
    "    print(r'余数既不是1也不是0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-08T09:46:45.061175Z",
     "start_time": "2023-10-08T09:46:45.057923Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 0 10\n"
     ]
    }
   ],
   "source": [
    "x = 5\n",
    "if x < 5:\n",
    "    y = -1\n",
    "    z = 5\n",
    "elif x > 5:\n",
    "    y = 1\n",
    "    z = 11\n",
    "else:\n",
    "    y = 0 \n",
    "    z = 10\n",
    "print(x, y, z)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## while循环"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-08T09:48:47.864357Z",
     "start_time": "2023-10-08T09:48:47.860282Z"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n"
     ]
    }
   ],
   "source": [
    "j = 0\n",
    "while j <10:\n",
    "    print(j)\n",
    "    j+=1 # avoid dead loop\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-10T09:52:42.140574Z",
     "start_time": "2024-10-10T09:52:42.134600Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "9\n",
      "25\n",
      "49\n",
      "81\n"
     ]
    }
   ],
   "source": [
    "j = 0\n",
    "while j <10:\n",
    "    if j%2 != 0: \n",
    "        print(j**2)\n",
    "    j+=1 # avoid dead loop "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-10T09:52:50.474979Z",
     "start_time": "2024-10-10T09:52:50.461041Z"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "9\n",
      "25\n",
      "49\n",
      "81\n",
      "121\n",
      "169\n",
      "225\n",
      "289\n",
      "361\n",
      "441\n",
      "529\n",
      "625\n",
      "729\n",
      "841\n"
     ]
    }
   ],
   "source": [
    "j = 0\n",
    "while j <50:\n",
    "    if j == 30:\n",
    "        break\n",
    "    if j%2 != 0: \n",
    "        print(j**2)\n",
    "    j+=1 # avoid dead loop\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-10T09:53:36.003755Z",
     "start_time": "2024-10-10T09:53:35.985834Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4\n",
      "3\n",
      "2\n"
     ]
    }
   ],
   "source": [
    "a = 4\n",
    "while a: # 0, None, False\n",
    "    print(a) \n",
    "    a -= 1\n",
    "    if a < 2:\n",
    "        a = {} # {}#[]#''#False #0 #None # []"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## try except "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-10T09:55:57.253704Z",
     "start_time": "2024-10-10T09:55:57.238770Z"
    },
    "code_folding": [],
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.0\n",
      "1.8\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[[1, 0, ZeroDivisionError('division by zero')]]"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def devidePlus(m, n): # 结尾是冒号\n",
    "    return m/n+ 1 # 注意：空格\n",
    "\n",
    "error = []\n",
    "\n",
    "for k, i in enumerate([2, 0, 5]):\n",
    "#     print(devidePlus(4, i))  \n",
    "    try:\n",
    "        print(devidePlus(4, i))\n",
    "    except Exception as e:\n",
    "        #print(i, e)\n",
    "        error.append([k, i, e])\n",
    "        pass\n",
    "error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-10-11T08:29:20.936465Z",
     "start_time": "2021-10-11T08:29:20.930314Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10.0\n",
      "10.0\n",
      "[0, 0, 1] 0 division by zero\n"
     ]
    }
   ],
   "source": [
    "alist = [[1,1], [0, 0, 1]]\n",
    "for i in alist:\n",
    "    try:\n",
    "        for j in i:\n",
    "            print(10 / j)\n",
    "    except Exception as e:\n",
    "        print(i, j, e)\n",
    "        pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-10T09:58:59.636261Z",
     "start_time": "2024-10-10T09:58:59.617344Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10.0\n",
      "10.0\n",
      "0 division by zero\n",
      "0 division by zero\n",
      "10.0\n"
     ]
    }
   ],
   "source": [
    "alist = [[1,1], [0, 0, 1]]\n",
    "for i in alist:\n",
    "    for j in i:\n",
    "        try:\n",
    "            print(10 / j)\n",
    "        except Exception as e:\n",
    "            print(j, e)\n",
    "            pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Write and Read data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-10-11T08:32:13.033454Z",
     "start_time": "2021-10-11T08:32:13.002901Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[0, 0, 0],\n",
       " [1, 1, 1],\n",
       " [2, 4, 8],\n",
       " [3, 9, 27],\n",
       " [4, 16, 64],\n",
       " [5, 25, 125],\n",
       " [6, 36, 216],\n",
       " [7, 49, 343],\n",
       " [8, 64, 512],\n",
       " [9, 81, 729]]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data =[[i, i**2, i**3] for i in range(10)] \n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-10-11T08:33:17.684527Z",
     "start_time": "2021-10-11T08:33:17.673861Z"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\t0\t0\n",
      "1\t1\t1\n",
      "2\t4\t8\n",
      "3\t9\t27\n",
      "4\t16\t64\n",
      "5\t25\t125\n",
      "6\t36\t216\n",
      "7\t49\t343\n",
      "8\t64\t512\n",
      "9\t81\t729\n"
     ]
    }
   ],
   "source": [
    "for i in data:\n",
    "    print('\\t'.join([str(j) for j in i]))\n",
    "    #print('\\t'.join(map(str, i)))  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-10-11T08:33:32.294118Z",
     "start_time": "2021-10-11T08:33:32.289809Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "list"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-10-11T08:33:34.533959Z",
     "start_time": "2021-10-11T08:33:34.529689Z"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-10-11T08:33:36.498179Z",
     "start_time": "2021-10-11T08:33:36.489166Z"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 0, 0]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-14T06:45:53.790335Z",
     "start_time": "2021-05-14T06:45:53.786988Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on built-in function write:\n",
      "\n",
      "write(text, /) method of _io.TextIOWrapper instance\n",
      "    Write string to stream.\n",
      "    Returns the number of characters written (which is always equal to\n",
      "    the length of the string).\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(f.write)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-14T06:47:52.384851Z",
     "start_time": "2021-05-14T06:47:52.359107Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "# 保存数据\n",
    "data =[[i, i**2, i**3] for i in range(10000)] \n",
    "\n",
    "f = open(\"data/data_write_to_file2023.txt\", \"w\")\n",
    "for i in data:\n",
    "    f.write('\\t'.join([str(j) for j in i]) + '\\n')\n",
    "f.close() \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-14T06:58:58.363840Z",
     "start_time": "2021-05-14T06:58:58.357336Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['0\\t0\\t0\\n', '1\\t1\\t1\\n', '2\\t4\\t8\\n', '3\\t9\\t27\\n', '4\\t16\\t64\\n']"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with open('data/data_write_to_file2023.txt','r') as f:\n",
    "    data = f.readlines()\n",
    "data[:5]\n",
    "# print(data[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-10-11T08:37:10.250695Z",
     "start_time": "2021-10-11T08:37:10.240232Z"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "77"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with open('./data/data_write_to_file1.txt','r') as f:\n",
    "    data = f.readlines(1000) #bytes 字节\n",
    "len(data) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-10-11T08:37:39.403613Z",
     "start_time": "2021-10-11T08:37:39.388782Z"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\t0\t0\n",
      "\n"
     ]
    }
   ],
   "source": [
    "with open('./data/data_write_to_file1.txt','r') as f:\n",
    "    print(f.readline())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-10-11T08:38:03.391379Z",
     "start_time": "2021-10-11T08:38:03.384342Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 0\t0\t0\n",
      "\n",
      "1000 1000\t1000000\t1000000000\n",
      "\n",
      "2000 2000\t4000000\t8000000000\n",
      "\n",
      "3000 3000\t9000000\t27000000000\n",
      "\n",
      "4000 4000\t16000000\t64000000000\n",
      "\n",
      "5000 5000\t25000000\t125000000000\n",
      "\n",
      "6000 6000\t36000000\t216000000000\n",
      "\n",
      "7000 7000\t49000000\t343000000000\n",
      "\n",
      "8000 8000\t64000000\t512000000000\n",
      "\n",
      "9000 9000\t81000000\t729000000000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# f = [7, 2, 10, 4, 5]\n",
    "\n",
    "# for k,i in enumerate(f):\n",
    "#     print(k,i)\n",
    "\n",
    "with open('data/data_write_to_file1.txt', 'r') as f:   \n",
    "     for k, i in enumerate(f):\n",
    "        if k%1000==0:\n",
    "            print(k, i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-10-11T08:40:43.014698Z",
     "start_time": "2021-10-11T08:40:32.941508Z"
    },
    "scrolled": true,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0000\n"
     ]
    }
   ],
   "source": [
    "#from time import sleep\n",
    "from tqdm import tqdm\n",
    "from time import sleep\n",
    "\n",
    "total = 0\n",
    "with open('./data/data_write_to_file1.txt','r') as f:\n",
    "    for i in tqdm(f):\n",
    "        sleep(0.001)\n",
    "        total+=1\n",
    "#     for k, i in enumerate(f):\n",
    "#         if k % 1000 ==0:\n",
    "#             sleep(1)\n",
    "#             print(k, end = '\\r')\n",
    "print(total)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-07T08:21:43.923393Z",
     "start_time": "2019-06-07T08:21:43.912523Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\t0\t0\n",
      "\n",
      "2000\t4000000\t8000000000\n",
      "\n",
      "4000\t16000000\t64000000000\n",
      "\n",
      "6000\t36000000\t216000000000\n",
      "\n",
      "8000\t64000000\t512000000000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "with open('./data/data_write_to_file.txt','r') as f:\n",
    "    for k, i in enumerate(f):\n",
    "        if k%2000 == 0:\n",
    "            print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-10-11T08:43:39.906095Z",
     "start_time": "2021-10-11T08:43:39.895309Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[0, 0, 0]]"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = []\n",
    "line = '0\\t0\\t0\\n'\n",
    "line = line.replace('\\n', '')\n",
    "line = line.split('\\t')\n",
    "line = [int(i) for i in line] # convert str to int\n",
    "data.append(line) \n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-10-11T08:44:52.632391Z",
     "start_time": "2021-10-11T08:44:52.569161Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[9995, 99900025, 998500749875],\n",
       " [9996, 99920016, 998800479936],\n",
       " [9997, 99940009, 999100269973],\n",
       " [9998, 99960004, 999400119992],\n",
       " [9999, 99980001, 999700029999]]"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 读取数据\n",
    "data = []\n",
    "with open('./data/data_write_to_file1.txt','r') as f:\n",
    "    for line in f:\n",
    "        line = line.replace('\\n', '').split('\\t')\n",
    "        line = [int(i) for i in line]\n",
    "        data.append(line)\n",
    "#len(data)\n",
    "data[-5:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-10-11T08:45:13.560049Z",
     "start_time": "2021-10-11T08:45:13.490087Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10000"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 读取数据\n",
    "data = []\n",
    "with open('./data/data_write_to_file.txt','r') as f:\n",
    "    for line in f:\n",
    "        line = line.replace('\\n', '').split('\\t')\n",
    "        line = [int(i) for i in line]\n",
    "        data.append(line)\n",
    "len(data)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-10-11T08:46:16.759923Z",
     "start_time": "2021-10-11T08:46:16.744863Z"
    },
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-05-11T22:18:54.212496",
     "start_time": "2017-05-11T22:18:54.207441"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function read_csv in module pandas.io.parsers:\n",
      "\n",
      "read_csv(filepath_or_buffer, sep=',', delimiter=None, header='infer', names=None, index_col=None, usecols=None, squeeze=False, prefix=None, mangle_dupe_cols=True, dtype=None, engine=None, converters=None, true_values=None, false_values=None, skipinitialspace=False, skiprows=None, skipfooter=None, nrows=None, na_values=None, keep_default_na=True, na_filter=True, verbose=False, skip_blank_lines=True, parse_dates=False, infer_datetime_format=False, keep_date_col=False, date_parser=None, dayfirst=False, iterator=False, chunksize=None, compression='infer', thousands=None, decimal='.', lineterminator=None, quotechar='\"', quoting=0, escapechar=None, comment=None, encoding=None, dialect=None, tupleize_cols=False, error_bad_lines=True, warn_bad_lines=True, skip_footer=0, doublequote=True, delim_whitespace=False, as_recarray=False, compact_ints=False, use_unsigned=False, low_memory=True, buffer_lines=None, memory_map=False, float_precision=None)\n",
      "    Read CSV (comma-separated) file into DataFrame\n",
      "    \n",
      "    Also supports optionally iterating or breaking of the file\n",
      "    into chunks.\n",
      "    \n",
      "    Additional help can be found in the `online docs for IO Tools\n",
      "    <http://pandas.pydata.org/pandas-docs/stable/io.html>`_.\n",
      "    \n",
      "    Parameters\n",
      "    ----------\n",
      "    filepath_or_buffer : str, pathlib.Path, py._path.local.LocalPath or any object with a read() method (such as a file handle or StringIO)\n",
      "        The string could be a URL. Valid URL schemes include http, ftp, s3, and\n",
      "        file. For file URLs, a host is expected. For instance, a local file could\n",
      "        be file ://localhost/path/to/table.csv\n",
      "    sep : str, default ','\n",
      "        Delimiter to use. If sep is None, will try to automatically determine\n",
      "        this. Regular expressions are accepted and will force use of the python\n",
      "        parsing engine and will ignore quotes in the data.\n",
      "    delimiter : str, default None\n",
      "        Alternative argument name for sep.\n",
      "    header : int or list of ints, default 'infer'\n",
      "        Row number(s) to use as the column names, and the start of the data.\n",
      "        Default behavior is as if set to 0 if no ``names`` passed, otherwise\n",
      "        ``None``. Explicitly pass ``header=0`` to be able to replace existing\n",
      "        names. The header can be a list of integers that specify row locations for\n",
      "        a multi-index on the columns e.g. [0,1,3]. Intervening rows that are not\n",
      "        specified will be skipped (e.g. 2 in this example is skipped). Note that\n",
      "        this parameter ignores commented lines and empty lines if\n",
      "        ``skip_blank_lines=True``, so header=0 denotes the first line of data\n",
      "        rather than the first line of the file.\n",
      "    names : array-like, default None\n",
      "        List of column names to use. If file contains no header row, then you\n",
      "        should explicitly pass header=None\n",
      "    index_col : int or sequence or False, default None\n",
      "        Column to use as the row labels of the DataFrame. If a sequence is given, a\n",
      "        MultiIndex is used. If you have a malformed file with delimiters at the end\n",
      "        of each line, you might consider index_col=False to force pandas to _not_\n",
      "        use the first column as the index (row names)\n",
      "    usecols : array-like, default None\n",
      "        Return a subset of the columns.\n",
      "        Results in much faster parsing time and lower memory usage.\n",
      "    squeeze : boolean, default False\n",
      "        If the parsed data only contains one column then return a Series\n",
      "    prefix : str, default None\n",
      "        Prefix to add to column numbers when no header, e.g. 'X' for X0, X1, ...\n",
      "    mangle_dupe_cols : boolean, default True\n",
      "        Duplicate columns will be specified as 'X.0'...'X.N', rather than 'X'...'X'\n",
      "    dtype : Type name or dict of column -> type, default None\n",
      "        Data type for data or columns. E.g. {'a': np.float64, 'b': np.int32}\n",
      "        (Unsupported with engine='python'). Use `str` or `object` to preserve and\n",
      "        not interpret dtype.\n",
      "    engine : {'c', 'python'}, optional\n",
      "        Parser engine to use. The C engine is faster while the python engine is\n",
      "        currently more feature-complete.\n",
      "    converters : dict, default None\n",
      "        Dict of functions for converting values in certain columns. Keys can either\n",
      "        be integers or column labels\n",
      "    true_values : list, default None\n",
      "        Values to consider as True\n",
      "    false_values : list, default None\n",
      "        Values to consider as False\n",
      "    skipinitialspace : boolean, default False\n",
      "        Skip spaces after delimiter.\n",
      "    skiprows : list-like or integer, default None\n",
      "        Line numbers to skip (0-indexed) or number of lines to skip (int)\n",
      "        at the start of the file\n",
      "    skipfooter : int, default 0\n",
      "        Number of lines at bottom of file to skip (Unsupported with engine='c')\n",
      "    nrows : int, default None\n",
      "        Number of rows of file to read. Useful for reading pieces of large files\n",
      "    na_values : str or list-like or dict, default None\n",
      "        Additional strings to recognize as NA/NaN. If dict passed, specific\n",
      "        per-column NA values.  By default the following values are interpreted as\n",
      "        NaN: `''`, `'#N/A'`, `'#N/A N/A'`, `'#NA'`, `'-1.#IND'`, `'-1.#QNAN'`, `'-NaN'`, `'-nan'`, `'1.#IND'`, `'1.#QNAN'`, `'N/A'`, `'NA'`, `'NULL'`, `'NaN'`, `'nan'`.\n",
      "    keep_default_na : bool, default True\n",
      "        If na_values are specified and keep_default_na is False the default NaN\n",
      "        values are overridden, otherwise they're appended to.\n",
      "    na_filter : boolean, default True\n",
      "        Detect missing value markers (empty strings and the value of na_values). In\n",
      "        data without any NAs, passing na_filter=False can improve the performance\n",
      "        of reading a large file\n",
      "    verbose : boolean, default False\n",
      "        Indicate number of NA values placed in non-numeric columns\n",
      "    skip_blank_lines : boolean, default True\n",
      "        If True, skip over blank lines rather than interpreting as NaN values\n",
      "    parse_dates : boolean or list of ints or names or list of lists or dict, default False\n",
      "    \n",
      "        * boolean. If True -> try parsing the index.\n",
      "        * list of ints or names. e.g. If [1, 2, 3] -> try parsing columns 1, 2, 3\n",
      "          each as a separate date column.\n",
      "        * list of lists. e.g.  If [[1, 3]] -> combine columns 1 and 3 and parse as\n",
      "            a single date column.\n",
      "        * dict, e.g. {'foo' : [1, 3]} -> parse columns 1, 3 as date and call result\n",
      "          'foo'\n",
      "    \n",
      "        Note: A fast-path exists for iso8601-formatted dates.\n",
      "    infer_datetime_format : boolean, default False\n",
      "        If True and parse_dates is enabled for a column, attempt to infer\n",
      "        the datetime format to speed up the processing\n",
      "    keep_date_col : boolean, default False\n",
      "        If True and parse_dates specifies combining multiple columns then\n",
      "        keep the original columns.\n",
      "    date_parser : function, default None\n",
      "        Function to use for converting a sequence of string columns to an array of\n",
      "        datetime instances. The default uses ``dateutil.parser.parser`` to do the\n",
      "        conversion. Pandas will try to call date_parser in three different ways,\n",
      "        advancing to the next if an exception occurs: 1) Pass one or more arrays\n",
      "        (as defined by parse_dates) as arguments; 2) concatenate (row-wise) the\n",
      "        string values from the columns defined by parse_dates into a single array\n",
      "        and pass that; and 3) call date_parser once for each row using one or more\n",
      "        strings (corresponding to the columns defined by parse_dates) as arguments.\n",
      "    dayfirst : boolean, default False\n",
      "        DD/MM format dates, international and European format\n",
      "    iterator : boolean, default False\n",
      "        Return TextFileReader object for iteration or getting chunks with\n",
      "        ``get_chunk()``.\n",
      "    chunksize : int, default None\n",
      "        Return TextFileReader object for iteration. `See IO Tools docs for more\n",
      "        information\n",
      "        <http://pandas.pydata.org/pandas-docs/stable/io.html#io-chunking>`_ on\n",
      "        ``iterator`` and ``chunksize``.\n",
      "    compression : {'infer', 'gzip', 'bz2', None}, default 'infer'\n",
      "        For on-the-fly decompression of on-disk data. If 'infer', then use gzip or\n",
      "        bz2 if filepath_or_buffer is a string ending in '.gz' or '.bz2',\n",
      "        respectively, and no decompression otherwise. Set to None for no\n",
      "        decompression.\n",
      "    thousands : str, default None\n",
      "        Thousands separator\n",
      "    decimal : str, default '.'\n",
      "        Character to recognize as decimal point (e.g. use ',' for European data).\n",
      "    lineterminator : str (length 1), default None\n",
      "        Character to break file into lines. Only valid with C parser.\n",
      "    quotechar : str (length 1), optional\n",
      "        The character used to denote the start and end of a quoted item. Quoted\n",
      "        items can include the delimiter and it will be ignored.\n",
      "    quoting : int or csv.QUOTE_* instance, default None\n",
      "        Control field quoting behavior per ``csv.QUOTE_*`` constants. Use one of\n",
      "        QUOTE_MINIMAL (0), QUOTE_ALL (1), QUOTE_NONNUMERIC (2) or QUOTE_NONE (3).\n",
      "        Default (None) results in QUOTE_MINIMAL behavior.\n",
      "    escapechar : str (length 1), default None\n",
      "        One-character string used to escape delimiter when quoting is QUOTE_NONE.\n",
      "    comment : str, default None\n",
      "        Indicates remainder of line should not be parsed. If found at the beginning\n",
      "        of a line, the line will be ignored altogether. This parameter must be a\n",
      "        single character. Like empty lines (as long as ``skip_blank_lines=True``),\n",
      "        fully commented lines are ignored by the parameter `header` but not by\n",
      "        `skiprows`. For example, if comment='#', parsing '#empty\\na,b,c\\n1,2,3'\n",
      "        with `header=0` will result in 'a,b,c' being\n",
      "        treated as the header.\n",
      "    encoding : str, default None\n",
      "        Encoding to use for UTF when reading/writing (ex. 'utf-8'). `List of Python\n",
      "        standard encodings\n",
      "        <https://docs.python.org/3/library/codecs.html#standard-encodings>`_\n",
      "    dialect : str or csv.Dialect instance, default None\n",
      "        If None defaults to Excel dialect. Ignored if sep longer than 1 char\n",
      "        See csv.Dialect documentation for more details\n",
      "    tupleize_cols : boolean, default False\n",
      "        Leave a list of tuples on columns as is (default is to convert to\n",
      "        a Multi Index on the columns)\n",
      "    error_bad_lines : boolean, default True\n",
      "        Lines with too many fields (e.g. a csv line with too many commas) will by\n",
      "        default cause an exception to be raised, and no DataFrame will be returned.\n",
      "        If False, then these \"bad lines\" will dropped from the DataFrame that is\n",
      "        returned. (Only valid with C parser)\n",
      "    warn_bad_lines : boolean, default True\n",
      "        If error_bad_lines is False, and warn_bad_lines is True, a warning for each\n",
      "        \"bad line\" will be output. (Only valid with C parser).\n",
      "    \n",
      "    Returns\n",
      "    -------\n",
      "    result : DataFrame or TextParser\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(pd.read_csv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-10-11T08:51:04.259933Z",
     "start_time": "2021-10-11T08:51:04.215541Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9995</th>\n",
       "      <td>9995</td>\n",
       "      <td>99900025</td>\n",
       "      <td>998500749875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9996</th>\n",
       "      <td>9996</td>\n",
       "      <td>99920016</td>\n",
       "      <td>998800479936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9997</th>\n",
       "      <td>9997</td>\n",
       "      <td>99940009</td>\n",
       "      <td>999100269973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9998</th>\n",
       "      <td>9998</td>\n",
       "      <td>99960004</td>\n",
       "      <td>999400119992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9999</th>\n",
       "      <td>9999</td>\n",
       "      <td>99980001</td>\n",
       "      <td>999700029999</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         a         b             c\n",
       "9995  9995  99900025  998500749875\n",
       "9996  9996  99920016  998800479936\n",
       "9997  9997  99940009  999100269973\n",
       "9998  9998  99960004  999400119992\n",
       "9999  9999  99980001  999700029999"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('./data/data_write_to_file2023.txt', \n",
    "                 sep = '\\t', names = ['a', 'b', 'c'])\n",
    "df.tail()\n",
    "#len(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## 保存中间步骤产生的字典数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-14T07:29:01.225030Z",
     "start_time": "2021-05-14T07:29:01.219271Z"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "import json\n",
    "data_dict = {'a':1, 'b':2, 'c':3}\n",
    "with open('./data/save_dict.json', 'w') as f:\n",
    "    json.dump(data_dict, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-10-11T08:52:51.877895Z",
     "start_time": "2021-10-11T08:52:51.862422Z"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'a': 1, 'b': 2, 'c': 3}"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import json\n",
    "dd = json.load(open(\"./data/save_dict.json\"))\n",
    "dd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "## 重新读入json"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## 保存中间步骤产生的列表数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-27T07:11:56.415515Z",
     "start_time": "2018-04-27T07:11:56.411564Z"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "data_list = list(range(10))\n",
    "with open('./data/save_list.json', 'w') as f:\n",
    "    json.dump(data_list, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-10-11T08:53:14.731694Z",
     "start_time": "2021-10-11T08:53:14.712935Z"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dl = json.load(open(\"./data/save_list.json\"))\n",
    "dl"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## 使用matplotlib绘图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-07T08:31:51.190898Z",
     "start_time": "2019-06-07T08:31:50.419496Z"
    },
    "code_folding": [],
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "x = range(1, 100)\n",
    "y = [i**-3 for i in x]\n",
    "plt.plot(x, y, 'b-s')\n",
    "plt.ylabel('$p(k)$', fontsize = 20)\n",
    "plt.xlabel('$k$', fontsize = 20)\n",
    "plt.xscale('log')\n",
    "plt.yscale('log')\n",
    "plt.title('Degree Distribution')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-07T08:32:20.805797Z",
     "start_time": "2019-06-07T08:32:20.592198Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "# red dashes, blue squares and green triangles\n",
    "t = np.arange(0., 5., 0.2)\n",
    "plt.plot(t, t, 'r--')\n",
    "plt.plot(t, t**2, 'bs')\n",
    "plt.plot(t, t**3, 'g^')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-07T08:33:26.540992Z",
     "start_time": "2019-06-07T08:33:26.292284Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# red dashes, blue squares and green triangles\n",
    "t = np.arange(0., 5., 0.2)\n",
    "plt.plot(t, t**2, 'b-s', label = '1')\n",
    "plt.plot(t, t**2.5, 'r-o', label = '2')\n",
    "plt.plot(t, t**3, 'g-^', label = '3')\n",
    "plt.annotate(r'$\\alpha = 3$', xy=(3.5, 40), xytext=(2, 80),\n",
    "            arrowprops=dict(facecolor='black', shrink=0.05),\n",
    "            fontsize = 20)\n",
    "plt.ylabel('$f(t)$', fontsize = 20)\n",
    "plt.xlabel('$t$', fontsize = 20)\n",
    "plt.legend(loc=2,numpoints=1,fontsize=10)\n",
    "plt.show()\n",
    "# plt.savefig('/Users/chengjun/GitHub/cjc/figure/save_figure.png',\n",
    "#             dpi = 300, bbox_inches=\"tight\",transparent = True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "plt.figure(figsize=(5,5))\n",
    "sns.set(style=\"whitegrid\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-07T08:34:00.646076Z",
     "start_time": "2019-06-07T08:34:00.215086Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(1)\n",
    "plt.subplot(221)\n",
    "plt.plot(t, t, 'r--')\n",
    "plt.text(2, 0.8*np.max(t), r'$\\alpha = 1$', fontsize = 20)\n",
    "plt.subplot(222)\n",
    "plt.plot(t, t**2, 'bs')\n",
    "plt.text(2, 0.8*np.max(t**2), r'$\\alpha = 2$', fontsize = 20)\n",
    "plt.subplot(223)\n",
    "plt.plot(t, t**3, 'g^')\n",
    "plt.text(2, 0.8*np.max(t**3), r'$\\alpha = 3$', fontsize = 20)\n",
    "plt.subplot(224)\n",
    "plt.plot(t, t**4, 'r-o')\n",
    "plt.text(2, 0.8*np.max(t**4), r'$\\alpha = 4$', fontsize = 20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "def f(t):\n",
    "    return np.exp(-t) * np.cos(2*np.pi*t)\n",
    "\n",
    "t1 = np.arange(0.0, 5.0, 0.1)\n",
    "t2 = np.arange(0.0, 5.0, 0.02)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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QwocKP+uAARnF3jG4/b0oi4Iuy91OaZ/XzQuF0xer0uRzU+F3BCYUev1w0VU+8DJwbaHX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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x108cbdd50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(1)\n",
    "plt.subplot(211)\n",
    "plt.plot(t1, f(t1), 'bo')\n",
    "plt.plot(t2, f(t2), 'k')\n",
    "\n",
    "plt.subplot(212)\n",
    "plt.plot(t2, np.cos(2*np.pi*t2), 'r--')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-05-13T09:23:13.850060",
     "start_time": "2017-05-13T09:23:13.255032"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Kbnk5ObzYvTtZR4+WrxwR5BxU2NZBVfc+2DBh0qguqQHgwLEDHDh2wPX15555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\nxsQlC1DGGGPi0v8HxId0HNqReosAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a2fa890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.gridspec as gridspec\n",
    "import numpy as np\n",
    "\n",
    "t = np.arange(0., 5., 0.2)\n",
    "\n",
    "gs = gridspec.GridSpec(3, 3)\n",
    "ax1 = plt.subplot(gs[0, :])\n",
    "plt.plot(t, t**2, 'b-s')\n",
    "ax2 = plt.subplot(gs[1,:-1])\n",
    "plt.plot(t, t**2, 'g-s')\n",
    "ax3 = plt.subplot(gs[1:, -1])\n",
    "plt.plot(t, t**2, 'r-o')\n",
    "ax4 = plt.subplot(gs[-1,0])\n",
    "plt.plot(t, t**2, 'g-^')\n",
    "ax5 = plt.subplot(gs[-1,1])\n",
    "plt.plot(t, t**2, 'b-<')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-07T08:35:14.708956Z",
     "start_time": "2019-06-07T08:35:14.695191Z"
    },
    "code_folding": [
     1
    ],
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "\n",
    "def OLSRegressPlot(x,y,col,xlab,ylab):\n",
    "    xx = sm.add_constant(x, prepend=True)\n",
    "    res = sm.OLS(y,xx).fit()\n",
    "    constant, beta = res.params\n",
    "    r2 = res.rsquared\n",
    "    lab = r'$\\beta = %.2f, \\,R^2 = %.2f$' %(beta,r2)\n",
    "    plt.scatter(x,y,s=60,facecolors='none', edgecolors=col)\n",
    "    plt.plot(x,constant + x*beta,\"red\",label=lab)\n",
    "    plt.legend(loc = 'upper left',fontsize=16)\n",
    "    plt.xlabel(xlab,fontsize=26)\n",
    "    plt.ylabel(ylab,fontsize=26)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-07T08:35:29.415424Z",
     "start_time": "2019-06-07T08:35:29.157757Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.random.randn(50)\n",
    "y = np.random.randn(50) + 3*x\n",
    "pearsonr(x, y)\n",
    "fig = plt.figure(figsize=(10, 4),facecolor='white')\n",
    "OLSRegressPlot(x,y,'RoyalBlue',r'$x$',r'$y$')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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qFI8//jjV1XKWv7Byly9DSgrY2MDUqWqnsViZwR7oNBrYuRMuXlQ7juggdqYW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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114197590>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(7, 4),facecolor='white')\n",
    "data = norm.rvs(10.0, 2.5, size=5000)\n",
    "mu, std = norm.fit(data)\n",
    "plt.hist(data, bins=25, normed=True, alpha=0.6, color='g')\n",
    "xmin, xmax = plt.xlim()\n",
    "x = np.linspace(xmin, xmax, 100)\n",
    "p = norm.pdf(x, mu, std)\n",
    "plt.plot(x, p, 'r', linewidth=2)\n",
    "title = r\"$\\mu = %.2f, \\,  \\sigma = %.2f$\" % (mu, std)\n",
    "plt.title(title,size=16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-07T08:36:49.566655Z",
     "start_time": "2019-06-07T08:36:49.550926Z"
    },
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>16</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   a   b   c\n",
       "0  0   0   0\n",
       "1  1   1   1\n",
       "2  2   4   8\n",
       "3  3   9  27\n",
       "4  4  16  64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv('../data/data_write_to_file.txt', sep = '\\t', names = ['a', 'b', 'c'])\n",
    "df[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-05-11T22:21:09.150592",
     "start_time": "2017-05-11T22:21:08.103623"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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5ubkpmymgteXm5rbq5ymAEvXYY/D88/Dee9C1a9StEZGIpHKutPZO14AS8c47cMYZUFgIe+7Z8v2JiKQx3Qe0E2Y2HLgL+JJQsuHWlHxQSUmYXufhhxU+IiIt0J6umu8DPOvulwL7peQT3OGSS+D888M9PyIikrC0DaAESnK/C1xqZm8A/0xJo+66C4qL4ZZbUrJ7EZFMkrbXgMzsCGAj8Ji7j42tywLmEFeSGzjP3WeZ2Y+B99z9LTN71t3PqWefiV8DmjULjjgCpk2DUaMS24eISBuUcdeAYkFSd0zg9pLcAGZWXZJ7FqHXc6OZXQgsTGpjqqrg0kvhxhsVPiLSLrg7W7dtZUP5BjaUbWjwcWP5xpS1IW0DqAGNleT+HNih11NXQiW5778/XP/5wQ8SarSISDKUV5ZvD4WdBceG8sbf21i+kQ7WgZ6de9Izu2etxy1zt7Bu1jrKvixj06pNKfs+bS2AWqzZJbmLi+GGG2DKFM10ICIJqaisYH3ZetaVrQuPW9c1/rrO+vVl69lQvoHKqsp6A6PWY+z50J5DG922R3YPsjtkN6n9qbrRtq0FUOuX5P75z8N0O2PGpPRjRCT9VHkVG8o2NCs86nuvvLKcXl16kdM5h5zOOfTqHJ736tKLnOyc7e8NyxlWs03c9jmdc+iZ3ZMuHbu0m1kXIP0DqMGS3MAKQknu81P26Z98Ai+8ALNnp+wjRCR13J2N5RtZu3VtraV0a2mT1m0o20C3Tt22h0FDwTGyz8ja79XZtmvHru0qOJIlnUfBRV+Se/x4OO00+OEPm9N0EUmi8spySreU8uWWL3cMjS1xoVFWz7qta+nSsQu9u/TevvTp2ic871zndfX7XWpe53TOoUNWh6gPQeRUkjsJmhVAU6fCBRfA3LmaaFQkCcq2lbFmy5odli+3fNnouq3bttKnSx/6dO2z/bGpAdKrS68mX+eQhimAkqBZAXTSSaHOz2WXpbZRIm1MlVdRuqWU1ZtXU7K5hJLNJSE0NscFydY1O6wrryynb9e+9S79uvbbcV23sK5ndk+dvoqYAigJmhxA778PZ50F8+ZB586pb5hIhDZXbKZkcwmrN4VAiQ+W1ZtWU7Kl9nulW0rJ6ZzDgO4D6N+tP/279a8/QOqs65HdQ0HSRimAkqDJAXTOOWHWg6uvTn2jRJJsc8VmijcWU7ypmOKNxazatIpVm1bVCpf4kKmsqmRA9wEM6BYCZUD3AfTv2r/mebf+td7r27UvHbPSffySJJMCKAmaFEBLl8LYsVBUBDk5rdIukca4OxvKN9QKlVqPm0LIVL+uqKxgUI9BDOo+aPvjwO4Dt/dWagVNt/5079RdPRNpVMZNxROZBx6ACy9U+EjKVVRWULypmOUblrNiwwqWb1genm9cUav3UrypmA7WYYdQGdR9EGMHjd0eMNXrczrnKFCkTVAPKF55OeTmwuTJsMcerdcwaVe2VW1j1aZV2wOlVsBsrHm9Zssa+nfrz9CeQxnacyhDegwJjz2HbA+agd0HMqj7ILpnd4/6a0kGS5sekJl1BU4AXnf3LcluUKReeQV2313hIw0q21bG0vVLWbJ+CUvWLWHJ+iXbX1eHTcnmknqDZdwu47Y/H9pzKAO7D9Q9JpLREjkF9xPgh8AdwC/N7HLgCmABcJO7T09i+1rXE0/AxRdH3QqJSEVlBcs3LK8VLkvWLWHphqXbX6/dupahPYcyPGc4w3sNZ3jOcPYasBfjdx3PLjm7bA8WXaQX2blmn4Izs18BDwJrgT2BKcB7wBPABcDVUYRQrH7QhYRQHePuR9SzTcOn4NauDaffFi2C3r1T2laJRtm2MpasX8LC0oUUrS1i4drwWLS2iEXrFrF602oG9RjE8JzhDMsZVitkqh8H9RhElmlSWsksaXMKDuji7gsAzOxcwIFvu/tMM3sW+BXwnSS2sUnc/S3gLTM7A5jW7B089xyccILCpw2rqKxgyfol20NlYelCitbVPF+9eTW79NyFvN55jOw9krzeeZw8+mTyeueR2yuXIT2HqOci0ooS+dfW3cxGE+rynAwsc/eZAO6+ysw2J6NhZvYQcCpQXF0RNbZ+ArXngru1zo9eAHy72R/43HPwnVbPTWmmsm1lLFy7kHlr5jH3y7nMWzOPeaXh+bINyxjcY3CtgDl+5PHk9c4jr3ceu+TsooARSSOJ/Gu8GXgDGAD0Bq6t8/76ljYqZiJwN/BY9YpYSe57iCvJbWYvuPus2PvDgbXu3rwKShs2hLnfnnkmSU2XliivLGf+mvnMXRMLmDXztj9fvmE5I3qNIL9vPqP7jmb3/rtzym6nMLrvaHJ752reL5E2pNkB5O5LzGxfwki4te7+HwAzm0EIprXJaFgCJbkhnPqb2OwPe+01OPRQ3fvTytaXrWdWySxmrp7JzJLYsnomi9ctZkSvEYzuN5r8PvnsOWBPTt/9dPL75pPbK5dOHTQ5rEh7kND5CHffCrxUZ/UCQknsi1raqEY0WJI71q4bd7aDektyv/RSKLsgKVGyuYTPVn3GF6u/qBU2a7euZfd+uzNmwBjG9B/DN8Z+gzEDxpDfN189GZEIFRYWUlhYSFFREUVFRSn7nLS+ETXWA3qp+hqQmZ0NjHf378VeXwQc7O5XNXF/O46Cc4fBg+Hdd2HkyKS2P9OUV5Yzq2QWnxR/UmvZXLGZvQfuzZ4D9mRM/zHbA2d4r+EaUSbSBqTNKDgz6w4cC7zj7iVx688C/u3u65LYvrqSX5L7iy+ge3eFTzOVbC7hg+UfMGPlDD5ZFYJm3pp5jOw9krGDxjJ20Fh+ePAPGTtoLMNzhmtqGBHZQSKn4O4FPge+Z2bnu/vG2PqpwF1mdom7VyWpfakvyV1YCAUFLdpFe7dmyxo+WP4B05dPZ/qK6Xyw/ANKt5ZywJAD2G/Qfpww6gR+fOiPGdN/DF07dY26uSLSRiRyI+rt7v5jMxsH7Ovuf4p77yBgnLvf1+KGtVZJ7nPOgdNP1wwIMVsqtjB9+XTeWfoO7y9/nw+Wf0DJ5hIOGHIABw45kIOGHsSBQw8kv2++Tp+JZIi0KcdgZr9y95/Gnt/u7j+u8/6d7v7/ktjGpNkhgNxh0CCYPh1GjGj4B9uxpeuXMnXJVKYumco7S9/hs1WfsffAvTl02KGMGzqOg4YexOh+oxU2Ihksba4BAX3NbLC7rwTqO9VW1sI2tZ65c6Fr14wJH3dnVsksJhdNZsqiKUxdMpUt27Zw2PDDOGzYYfz2hN9y0NCDdBpNRFpFIgF0H/Cqmf2A+gNoeMua1Irefx8OPnjn27VR7s780vlMXjiZyUVhye6QzbEjj2VC/gRuPuZm8vvma4CAiEQikRtRZ5jZ74E3gTVm1pNwD1BHYAJhoEDbMH06HHRQ1K1IqtItpbw2/zX+Of+fvLHgDaq8imPyjuG4kcdxy7G3MLL3SAWOiKSFhO8DMrNDgRsJQ7I7EKbgeQD4H3evTFYDk2mHa0BHHgk33gjHHRdZm1qqyqv4aMVHvDrvVV6d9yqfFn/KUblHcVL+SZyw6wmM7jtagSMiLZI2gxB22IFZR8JItZJ0DZ5qtQKosjLMfL1kSZubAbtsWxmTFk7i+ZnP89Kcl+jVpRcn5Z/EyaNP5qjco+jSsUvUTRSRdiSdBiHU4u7bzKwbcKeZzQYedPf0H4gwe3aYAaGNhM+m8k38a/6/eH7m87w892X2HLAnZ485m+uOuI5d++4adfNERJotkZkQXgAqgUnAm+7+qbsvBK40s72A3xEqpKa3L76AvfaKuhWNqqis4PUFr/P4J4/zytxXGDd0HGeNOYvfnPAbhvYcGnXzRERaJJEe0OuECUd/B3Qwsy8JVVELgflAXrIa1xwWLnTcDOQA77v7443+wMyZMGZMazStWdyd6cun88QnT/DU508xqs8oLh57MXefdDf9u/WPunkiIkmTyCi4e4B7YnPCHQ4cCRwN3EYYjHB9UlvYdGcQ5oYrIcyS3bgvvoCTTkp1m5ps3dZ1PP7J49w//X62btvKRWMv4u1vv01+3/yomyYikhIJXwOKFX17LbZgZl0I4fNqMhqWQEXU3YG33f3BWGnwyY1+wMyZcM01yWhqi3yw/APun34/z818jhN3PZF7Tr6Ho3OP1sg1EWn3klafOFYj6OdmdieQjKl4mlsRdSk1szA0PhqvshLmzIE99khCM5uvyqt4ec7L/Gbqb1i8bjGXHXgZM6+YyeAegyNpj4hIFBIZhPB14KfAHOAJ4FV3r4jfJBkNS6Ai6vPA3WZ2JOEm2YYtXgz9+kHPnsloapOVbSvjyU+f5Lapt9G1U1d+cthPOHvPs+mYlbS/A0RE2oxEfvNdBDxMmPXgeWCDmU0GZgN9gbqhkUwNVkR19y3ApTvbQUFBAXmdO5OXlUVBYWGoiJpi26q28ciMR7h5ys3s0X8P7jn5Ho7JO0an2UQkLbVWRdREAmgRcK+732VmQ4GvAycCpwNzgcuT2L6kKywshIkTYfLklNcBqvIqnv7saW4ovIFhOcN46uynOHT4oSn9TBGRliooKKj1x3mq/lhOJIDuBP5gZv8EXnT3O4A7ktusBiWnImpREeTlJadFDXh36bv88JUf0iGrA/edch/HjWq70/2IiKRCs4u8uPs84PvABmBA0ltUW4MVUc0sm1AR9cVm7zWFAbRy40q+9fdvcfYzZ3P1V67m3e+8q/AREalHowFkZrvXt96DN2I1gVIiVhF1KrCbmS2OlfquBK4kDP3+HHjK3Wc2e+eLFiU9gNydR2c8ytj7xjKw+0BmXTGLi/e9WNd5REQa0OhkpGa2EdhCzUwHhe7+aT3bjQSuJfRQJrp7fXWCIrd9MtK8PJg0CUaNSsp+l61fxmX/uIyl65fyyJmPsN/g/ZKyXxGRdJCqyUh3dgpuNeF+nH6EmQ5mmNkqM3vOzH5oZnsDuPtCd/8+8DHw+2Q3MqmqqmD5chg2LCm7e2n2SxzwxwM4aOhBTPvuNIWPiEgT7WwQwlR3/wlsn+ngcKAAOAa4HegYNxfcJMIouJEpa20yfPlluP8nO7tFu6morOBnk37GXz77C3/7+t84bPhhSWqgiEhmaDSA3P3CuOdbgX/HFmIlGI6kJpB+Txgw8NMUtTU5Vq6EQYNatIvSLaWc9cxZdO7QmQ8v+1CThIqIJKAlc8FtBv4VWzCzrkAnd1+fpLalRnFxqAOUoIWlCzn5yZM5Kf8kbjvhNjpkdUhi40REMkezh2E3xN23pH34QIt6QB+v/JjDHz6cK8Zdwe/G/07hIyLSAkmZhMzM8oAfA18SZklYnYz9pkSCPaAZK2cw4YkJ3H3S3Zyz1zkpaJiISGZJZDLSKUB3QrmDQmCKuxcRKqL2A26lCXOyRSaBHtDHKz9mwhMTuPfkezl7z7NT1DARkcySSA/ogdgyELgGqDSzGYQwWkBqJyNtueLiZpXiXrxuMaf+5VTuPuluhY+ISBIlEkBHAbu5+3IzG0EYAXcUcA6hZ3RlEtvXZGZ2NKEk9+fAX9x9Sr0brl4NA5o2g9DarWs5+c8nc80h1+i0m4hIkiUSQKXuvhzA3RcDjwKPmllnQgG5j5LYvuZwwvx0nWmsJHdpKfTps9OdVXkVF//tYgryCvh/hySjvp6IiMRLZBTcrmY2vO5Kdy8jTFL6gxa3ilCS28yKzeyTOusnmNksM5tjZtfGff4Udz8FuA64qcEdr13bpAC6fertlGwu4Y7xd2g+NxGRFEgkgO4DPjCz882s1jjk2Bxw25LSsjAF0Pj4FXEluccDewHnm1ndutprgYanOSgthd69G/3g95a+x2/f+S1Pf+1pOnXolEDTRURkZ5p9Cs7dJ5nZTwmn3u4xs38B7wKLgVHA6GQ0rLkluc3sq4Rg6kUIqfqtXdtoAJVtK+PbL36buybcxYheIxrcTkREWiah+4Dc/SEzmw78H2HwwXmxtz6KvU6Vxkpy/w342852UFBRQd7ll5OXl7dD1T+AX731K/L75nPuXucmr9UiIm1I2pbkNrOewBigxN1PNbMROZ6uAAATs0lEQVReQD6wLlasLq0VDhwIjzxS73sLShdwz7R7+Pj7H+u6j4hkrHQuyT2HcA/Qe2b2IVBKOAX3YTIb1oCWl+RuZADC9ZOu5+qvXM0uObsk1DgREWm6RALop4RCdhOrV5jZccDtZvaCu7+ZtNY1UpIbWEE49Xd+s/bYwPWf6cunM2XRFP502p8Sa6mIiDRLs0fBufsj8eETW/dvd7+GMET7jGQ0LGUluXv1qnf1zVNu5n+O+B+6Z3dvWcNFRKRJkjIZaTV3f9jMbgdeSMK+Lmhg/avAqwnvuGfPHVbNXD2Td5e+y1NnP5XwbkVEpHma3QMys6+b2Qwze8bMTo7dmxOvS5Lalhrdd+zh3P7O7Vwx7gq6duoaQYNERDJTIj2gi4CHgQnAi8A6MysklOMeSpgPLn1161br5bqt63jui+eYe+XciBokIpKZEpkJYRFwj7ufTJj5+rdADnA6YQaCHyaveSlQpwf09OdPc/yo4xnQvWkTlIqISHIkEkB3AfeZ2WnASnf/lbuf4O57uvt57r4iyW1Mrjo9oIkzJnLJfpdE1BgRkcyVyCi4OcDlQAXhfqC2Ja4HtLB0IQtKFzA+f3wjPyAiIqmQ6FQ8VcA/k9yW1hEXQC/MfoHTdjuNjllJHQwoIiJNkMgpuLYt7hTci7Nf5PTdT4+wMSIimatdBZCZdTOz983s5AY3ivWASreUMn35dI4fdXxrNU9EROK0qwACrgWebnSLWA9oyqIpHDr8ULp16tbo5iIikhppG0DNrYhqZscDXwCrqT1/XG2xHtCURVM4OvfolLRdRER2Lm0DiOZXRC0AvgJcAFza4F6re0CLp3BU7lHJbrOIiDRR2g7/am5FVHf/WWzdN4CSBnfcpQsbyjYwc/VMxg0dl5rGi4i0Ue5QUQHl5TVLqqRtADWgwYqo1dz9scZ2UHDppXTJ7Ufv8t6889Y7O1REFRFpDe6wbRuUldUs5eW1X9e3rinbJPpz1YHToUMhWVmFQFFsSY22FkAtVvjUU9xd8gojV49U+IhkoOpf/Fu3Jn9p7i//rCzo3HnHJTu7+et69IB+/erfrjn77NQJzAoIVzWCdKqIGqWWV0TNzmbGyhmM20Wn30SiVFUVfmlv2RKWzZtrP1Y/T0VQZGVBly6JL926Qd++Na87d655bOov++xs6NAh6v8K0Ur3AEp+RdTsbGYUz+C7B343aY0UaS+qqmp+8dcNgvrCoSXrysrCL+KuXcPSrVvtx+rnXbqE540FQHOWzp2hY7r/5ssQafufIVYRtQDoZ2aLgRvcfaKZVVdEzQIeam5F1KpOHfli9RfsPXDvpLdZpDVUVsKmTclbNm6seb51a80v+LpBUF84VD927w79++98u7rBkpXO43Al5dI2gFJVEXXZ1tX07dqXHtk9Em6bSFO5h7/2N2xo/hIfDPFBUV5e80u/vqVHj9qve/aEwYMb3j5+6dZNoSCtJ20DKFXmb1zCrn12jboZkua2bYN162qWtWtrv16/vukhkp0dQmBny+DBMHp0zesePXYMk+7dQw8iRdeERVpVxgXQvA1F7NpXAdSeVfc61qwJwVE3POJfN/Teli2QkwO9e0OvXjVL795hffV7w4fvPFh0vUGkfhn3T2N+6QL1gNqIqqrQ01izZudLaWnt1x07Qp8+ISTiQ6T6eZ8+kJdXf8D06hV6HupliKRWBgbQfL66x1ejbkZG2rwZVq/e+VJSEkJk3boQBH371r/ssgvss8+O6/v0CRe4RSS9ZVwALduwjOG9hkfdjHbBPYTEihWwcmV4rH5eX7Bs2wYDBtS/5OfXPO/XLyy9e+v0lUh7lnH/vFdsWMHgHoOjbkZacw+ntJYsgeXLa4KlbtCsWBHumh4yJFxAHzKk5vmYMbUDZuBAndYSkdoyL4A2rmBIjyFRNyNSGzeGcFmyBBYvrnkev3TqFC6w77JLTajsthscfXTtwImrcC4i0iwZF0CdsjrRPbt9/9asqAjBsmBBzTJ/fnhcuDDcgT58eM0yYgQcdljtdT17Rv0tRKS9azcBFKsLdDXQD5jk7vfXt117Of1WVQWLFsGsWTBzJsyeXRMyy5aF3smoUWHZdVc455zwfOTIcH1Fp8JEJGrtJoDcfRZwuYVpWx8F6g2gIT3b1um3bdtCuHz2WQiaWbPCMmdOCJIxY2CPPWDsWDjrrBAyubnh5kcRkXSWtgFkZg8BpwLF7j42bv0E4E5q5oK7Ne6904DvA483tN/+3fqnrM0ttXYtfPIJfPwxzJgRHr/4AoYNC8ONx4yB006D//7vcD1Gp8lEpC1L2wAilOS+G9heYC6uJPdxwHLgfTN7Idb7wd1fAl4ys38AT9W30z5d+qS63U1SURHC5t13a5YVK0LQ7LcfjBsH3/0u7L13GD0mItLepG0ANbckt5kdDZwFdAZebmi/vbv0TlGLG7dpE7z9NkyaFB4/+ihcjznkECgogOuuC6fSMr0+iIhkjrQNoAY0WJLb3d8E3tzZDv76k79S8mQJeXl5FBQUpKwqamUlvPMOvP46TJ4MH34I++8Pxx4LN9wABx8c5hMTEUk3hYWFFBYWUlRURFFRUco+p60FUItd88A1XPmVK1Oy7w0b4LXX4MUX4ZVXwj00EybAz34Ghx+ue2ZEpG2o+8e5SnIHLS7JnexTcGVl8PLL8Oc/h97OoYfC6afDTTeF0WgiIlK/dA+gpJfkTlYAffwx3HcfPPss7LsvXHghPPRQmL9MRER2Lm0DKFUluVsSQJWV8PzzcPfd4YbP738/BNGwYQnvUkQkY5m7R92GVmNm/tGKj9hv8H7N+rnKSnjqKbj55jDd/49+BGeeGeZLExFp78wMd0/6haC07QGlSteOXZu1/b//DVddFU6t3XMPHHecprEREUmGzAugTk0LoJUrQ/BMmwZ33glnnKHgERFJpqyoG9DamtIDevnlcM/OrruGqXDOPFPhIyKSbOoBxXGHX/wCHnsMnnkGjjyyFRsmIpJhMi+AGugBVVTAN78Z6uW8/36o4CkiIqmTcQHUIWvHydYqK+Gii8J8bZMmQdfmjVMQEZEEZFwA1efKK2HNGnjpJejSJerWiIhkhowPoIkTQ6/n/fcVPiIirand3IhqZmcApwA9gYfd/fV6tvH477tgQZiVesoU2HPP1muriEhbkqobUdtNAFUzs97Abe7+3Xre2x5A7nDyyaEWz7XXtnIjRUTakFQFUNreB2RmD5lZsZl9Umf9BDObZWZzzKy+6PgZcO/O9v/662HE2zXXJKvFIiLSHGkbQISS3OPjV8SV5B4P7AWcb2Z7xL3/a+AVd5/R2I7d4X//N9zzo/ncRESikbYB5O5vAaV1Vm8vye3uFUB1SW5is2QfB3zNzL7X2L4/+ABWrICvfz0FDRcRkSZpa6PgGivJfTdw9852UFBQwMqVeeTm5vGf/6SuJLeISFulktwp8sYbhQweDP/6lyqWiojUp7VKcqftKbgGtLgk97RpoYCcwkdEJFrpHkANluQ2s2xCSe4Xm7PD116DE09MYgtFRCQhaRtAsZLcU4HdzGyxmV3i7pVAdUnuz4GnmluS+8034dhjk99eERFpnnZ3I2pjzMx79XLmzoUBA6JujYhI25BxN6KmSs+eCh8RkXSQcQG0775Rt0BERCADA2jUqKhbICIikIEBlJcXdQtERAQyMIB0/4+ISHrIuAAaMWLn24iISOplXAD17x91C0REBNpRAJnZSDP7k5k909h2ffu2VotERKQx7SaA3H2hu1+6s+1yclqjNSIisjNpG0AtqIi6k/0mr40iIpK4tA0gEqiIWr1Z6zRPRERaIm0DKIGKqH3N7D5gv0R6RiIi0rraWkG6xiqirgEu39kOCgoKyMvLIy8vb4eiSyIiooqoKVNYWBh1E0RE0poqotavxRVRRUQkPaR7ACW9IqqIiKSHtA2gVFVEFRGR9JBxFVEz6fuKiCSDKqKKiEi7ogASEZFIKIBERCQSCiAREYmEAkhERCKhABIRkUgogEREJBIKIBERiUS7mYzUzLoBfwDKgDfd/cmImyQiIo1oTz2gs4Bn3f0y4PSoG5PuNCt4DR2LGjoWNXQsUi9tAyiBktzDqKkVVNlqDW2j9I+rho5FDR2LGjoWqZe2AUTzS3IvIYQQqCy3iEjaS9sAam5JbuBvwNfM7F7gpdZrqYiIJCKtZ8M2s1zgJXcfG3t9NjDe3b8Xe30RcLC7X9XE/aXvlxURSWOpmA273YyCa4pUHEAREUlM2p6Ca4BKcouItBPpHkAqyS0i0k6lbQCpJLeISPuWtgHk7he4+1B37+zuI9x9Ymz9q+6+u7uPdvdfN2Vfjdw71G6Y2TAzm2Rmn5vZp2Z2VWx9HzN7zcxmm9m/zKxX3M/81MzmmtlMMzsxbv0BZvZJ7HjdGcX3SQYzyzKzD83sxdjrjDwWZtbLzJ6NfbfPzewrGXwsfmRmn8W+x5/NLDtTj0VacPd2vRBCdh6QC3QCZgB7RN2uFHzPwcB+sec9gNnAHsCtwE9i668Ffh17vifwEWEgSl7sGFWPinwPGBd7/gph5GHk3zGBY/Ij4AngxdjrjDwWwCPAJbHnHYFemXgsgKHAAiA79vpp4JuZeCzSZUnbHlASNXbvULvh7ivdfUbs+UZgJmGQxhnAo7HNHgXOjD0/nXAKc5u7FwFzgYPNbDDQ093fj233WNzPtBlmNgw4GfhT3OqMOxZmlgMc6TVnELa5+zoy8FjEdAC6m1lHoCthEFOmHovIZUIA7ULNFD0AS2Pr2i0zywP2A94FBrl7MYSQAgbGNqt7XJbF1u1COEbV2urxugP4byD+3q9MPBYjgRIzmxg7HfnH2MS9GXcs3H05cDuwmPC91rn7G2TgsUgXmRBAGcXMegDPAVfHekJ1b75t9zfjmtkpQHGsR9jYvV/t/lgQTh8dANzr7gcAm4DryMz/L3oTeju5hNNx3c3sQjLwWKSLTAigjLl3KHZa4TngcXd/Iba62MwGxd4fDKyKrV8GDI/78erj0tD6tuRw4HQzWwD8BTjWzB4HVmbgsVgKLHH36bHXfyUEUib+f3E8sMDd13gYUfs34DAy81ikhUwIoEy6d+hh4At3/33cuheBb8WefxN4IW79ebFRQCOBfGBa7BTEOjM72MwM+Ebcz7QJ7v4/HkZOjiL8957k7hcT5gj8VmyzTDkWxcASM9sttuo4wi0MGff/BeHU2yFm1iX2HY4DviAzj0V6iHoURGsswATCqLC5wHVRtydF3/FwQhmKGYSROx/Gvndf4I3Y938N6B33Mz8ljOyZCZwYt/5A4NPY8fp91N+thcflaGpGwWXksQD2JfwhNgN4njAKLlOPxQ2x7/UJYcBBp0w9FumwpPVkpCIi0n5lwik4ERFJQwogERGJhAJIREQioQASEZFIKIBERCQSCiAREYmEAkhERCKhABJpJjPrG6sRc02S9/vbWH2ZdWY2Ppn7FklHCiCR5usCrAc+TuZO3f2/CLN4dyPMZC7SrnWMugEibY2Haf0PTNHuxwGfeKjZI9KuqQckkl4KgMKI2yDSKhRAImnCzEYQSj8XRtsSkdahU3AiTWRmewFXEGrBPOHuT8fWn06oNbMvYTr/vsA5sR87Avilu79az/6OBS4H5gPdCaUBKoEp9Wy7X2zbNUA20A+4wt03xd4/ALiIUEwtF/gucBnQm1Ct8xfuvrDFB0EkmaKejluLlrawEKbtv59QYfVqYEbc+t/Fnk8D/gP8KO7nrgUW1bO/7xAKnw2PvR5MGNgwvZ5tv0UosTE4bt1/A6fFnucTVxIAmEgoLXAIoeBaZXybtGhJl0Wn4ESa5qvAM+7uwKmE3gqEekNvxZ6PApa7+x1xP2eEHlHNCrN9gfsIZdOXAHisyBl1Tr+Z2aHAH4EfxLbBzMYBRwHvxDb7EaHMdrXuwBp3f5dQhO124JFEvrRIKqkekEgTmNkwd19qZqMIRcjGu/sbsVLOa4HdCQXfDnf3d+J+7i+EXs4Rcev+ARwKDPRQGhozywUWAqe7+z/itv0X4dTeo4RrtlmE4mhPufvG2DbDq4Ms9nopMNHdf56KYyGSLLoGJNIE7r409vRSwim1N2Lri2H79ZxNhMqjxNZ1JFSl/VXcul7AeODZ6vCJOYY6139iP38s8KC7X9tI2+LDZw9gKDA5oS8q0op0Ck6kicysA+F6zJ9ir/Pi3i4A3nb3bXHrTgZ6Ak+ZWVZslFs+0IEdbzQ9CvjY3deb2cjYun6xbRc0o5nHA2XA1Lh2j2x4c5HoKIBEmm4CMACYGOudXA5gZlmEAKnb67gYmOzuiwnBsD/hOg+EazPEfr4zcBwwKbaqeoqfEsLAhB3OVJjZYDM7y8y6mNmtsRF6xD7nE3ffGtvOgP9K/CuLpI4CSKTpdiP8cl8BfB94PLb+AMJw58J6tn8x1nM6F/iHu88jjGgbBdvD627C9DtzzWwAYXQcsVN0DxCCbzszOwi4Ffg3oZf1X8BeZrZ7bL9lcZtfDzzW0i8ukgoahCDSRGY2BHgS+IBwuuzx2PrTgJuBA9y9Km77cwj3BS0hDJOeFVs/CriTMJihI/AHQu/oUsJAhOvc/cvYth2AXxPu5VkCdCYMsX7A3avMrB8hjL6MfeyNsf1tBcqBF93936k4HiItpQASEZFI6BSciIhEQgEkIiKRUACJiEgkFEAiIhIJBZCIiERCASQiIpFQAImISCQUQCIiEgkFkIiIROL/A6Y0VdlQxROWAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a3fc048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.line()\n",
    "plt.yscale('log')\n",
    "plt.ylabel('$values$', fontsize = 20)\n",
    "plt.xlabel('$index$', fontsize = 20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-05-11T22:32:42.105831",
     "start_time": "2017-05-11T22:32:41.789998"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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J4Gp3761y/H3AGnf/TpX9ChhpSpMnT+fw4QNlrYX3jMK1LoE1a9bw0Y9qoXQJxBkwqQ6R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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118312150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.scatter(x='a', y='b')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-05-11T22:26:59.893837",
     "start_time": "2017-05-11T22:26:59.528857"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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7/Q/AcFX9voiMA+apapWInAo8BpxLfOhkHtC9FzFVNSYitwBziU9ZfEBVV4vI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\nwI0xpkBZgBtjTIH6/0bAIoOSa0aZAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1136cc310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.hexbin(x='a', y='b', gridsize=25)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-05-11T22:28:39.025521",
     "start_time": "2017-05-11T22:28:38.630099"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1138f4350>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['a'].plot.kde()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-05-11T22:37:06.791001",
     "start_time": "2017-05-11T22:37:06.226303"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/chengjun/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:1: FutureWarning: \n",
      "The default value for 'return_type' will change to 'axes' in a future release.\n",
      " To use the future behavior now, set return_type='axes'.\n",
      " To keep the previous behavior and silence this warning, set return_type='dict'.\n",
      "  if __name__ == '__main__':\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x113a708d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bp = df.boxplot()\n",
    "plt.yscale('log')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-05-11T22:39:16.120016",
     "start_time": "2017-05-11T22:39:15.913844"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a0b9890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['c'].diff().hist()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-05-11T22:40:14.418943",
     "start_time": "2017-05-11T22:40:14.037032"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11905c310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.hist(stacked=True, bins=20)\n",
    "# plt.yscale('log')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "> To be a programmer is to develop a carefully managed relationship with error. There's no getting around it. You either make your accommodations with failure, or the work will become intolerable.\n",
    "\n",
    "Ellen Ullman  （an American computer programmer and author）\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## This is the end.\n",
    "> Thank you for your attention."
   ]
  }
 ],
 "metadata": {
  "celltoolbar": "幻灯片",
  "kernel_info": {
   "name": "python3"
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  },
  "latex_envs": {
   "LaTeX_envs_menu_present": true,
   "autoclose": false,
   "autocomplete": true,
   "bibliofile": "biblio.bib",
   "cite_by": "apalike",
   "current_citInitial": 1,
   "eqLabelWithNumbers": true,
   "eqNumInitial": 0,
   "hotkeys": {
    "equation": "Ctrl-E",
    "itemize": "Ctrl-I"
   },
   "labels_anchors": false,
   "latex_user_defs": false,
   "report_style_numbering": false,
   "user_envs_cfg": false
  },
  "nteract": {
   "version": "0.15.0"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": false,
   "sideBar": false,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {
    "height": "48px",
    "left": "1263px",
    "top": "0px",
    "width": "159px"
   },
   "toc_section_display": false,
   "toc_window_display": false
  },
  "toc_position": {
   "height": "339px",
   "left": "1245.99px",
   "right": "20px",
   "top": "120px",
   "width": "196px"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}